Riyad Alrousan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Riyad Alrousan has a total of 12 co-authors. Here are the top co-authors by number of shared publications:
- Hussam Nawwaf Fakhouri
Department of Intelligent Systems, Faculty of Artificial Intelligence, Al-Balqa Applied University, Al-Salt 19117, Jordan
6 shared publications - Faten Hamad
Sultan Qaboos University, Oman & University of Jordan, Jordan
5 shared publications - Zaid Khrisat
Faculty of Arts and Educational Sciences, Middle East University, Amman 11831, Jordan
4 shared publications - Jamal Zraqou
University of Petra, Jordan
3 shared publications - Najem N. Sirhan
Computer Science Department, Faculty of Information Technology, University of Petra, Amman 11196, Jordan
3 shared publications - Niveen Halalsheh
Department of Journalism, Media, and Digital Communication, School of Arts, The University of Jordan, Amman 11942, Jordan
3 shared publications
Latest Publications
- Open Access
- 28 October 2026
- Published by MDPI AG in Future Internet
- Vol. 18 (11) , 586
Where 5G Throughput Prediction Actually Fails: A Transition-Aware, Leakage-Audited Evaluation on Real Drive-Test Traces
Machine-learning models that predict short-horizon cellular throughput underpin adaptive video streaming, cloud gaming, and edge orchestration. On the widely used Raca et al. 5G drive-test dataset, such models can report low error. We show that, on this dataset, these headline numbers can be optimistic and, more importantly, that aggregate metrics conceal where and how the models fail. Using the saturated file-download sessions, where throughput reflects deliverable capacity rather than application idling, we (i) quantify the optimism of random-row evaluation relative to a deployment-realistic leave-sessions-out (LOSO) protocol, negligible at a 1 s horizon but reaching ∼32% at 8 s, and show that a trivial persistence baseline significantly beats the learned models at the two shortest horizons; (ii) show by ablation that most reported accuracy is carried by autoregressive throughput features rather than radio/context indicators and that the indicator-only mapping generalizes poorly across sessions; (iii) find, contrary to the intuition that handovers dominate error, that absolute error is lower around transitions and concentrated in the high-throughput regime (Gini 0.71), but that, in scale-invariant terms, the error is largest at low throughput and that transitions carry only a modest, noisy under-prediction tendency (event-aligned trough ≈ Mbps) that nearly vanishes under linear-space training (≈−1 Mbps), i.e., largely a log-space retransformation artifact; and (iv) find, using paired per-session bootstrap tests, that lightweight transition-context features give no significant global gain, a (mostly) null result. All headline numbers carry per-session bootstrap confidence intervals; the self-lag finding further replicates on an independent HSDPA drive-test dataset from Australia. We recommend leave-sessions-out evaluation, a persistence baseline, self-lag ablation, and scale-invariant, bias-aware reporting as a minimal honest protocol.- 28 October 2026
- Published by MDPI AG in Future Internet
- Vol. 18 (11) , 586
Open AccessWhere 5G Throughput Prediction Actually Fails: A Transition-Aware, Leakage-Audited Evaluation on Real Drive-Test Traces
Machine-learning models that predict short-horizon cellular throughput underpin adaptive video streaming, cloud gaming, and edge orchestration. On the widely used Raca et al. 5G drive-test dataset, such models can report low error. We show that, on this dataset, these headline numbers can be optimistic and, more importantly, that aggregate metrics conceal where and how the models fail. Using the saturated file-download sessions, where throughput reflects deliverable capacity rather than application idling, we (i) quantify the optimism of random-row evaluation relative to a deployment-realistic leave-sessions-out (LOSO) protocol, negligible at a 1 s horizon but reaching ∼32% at 8 s, and show that a trivial persistence baseline significantly beats the learned models at the two shortest horizons; (ii) show by ablation that most reported accuracy is carried by autoregressive throughput features rather than radio/context indicators and that the indicator-only mapping generalizes poorly across sessions; (iii) find, contrary to the intuition that handovers dominate error, that absolute error is lower around transitions and concentrated in the high-throughput regime (Gini 0.71), but that, in scale-invariant terms, the error is largest at low throughput and that transitions carry only a modest, noisy under-prediction tendency (event-aligned trough ≈ Mbps) that nearly vanishes under linear-space training (≈−1 Mbps), i.e., largely a log-space retransformation artifact; and (iv) find, using paired per-session bootstrap tests, that lightweight transition-context features give no significant global gain, a (mostly) null result. All headline numbers carry per-session bootstrap confidence intervals; the self-lag finding further replicates on an independent HSDPA drive-test dataset from Australia. We recommend leave-sessions-out evaluation, a persistence baseline, self-lag ablation, and scale-invariant, bias-aware reporting as a minimal honest protocol. - Open Access
A Blockchain and Federated Learning Framework for Image-Based IoT Malware Detection and Prevention
Internet of Things (IoT) devices are increasingly targeted by rapidly evolving malware, yet collaborative detection remains challenged by privacy leakage, noisy and imbalanced training data, and weak integrity guarantees when sharing model updates. This paper presents Mal-Fedchain, a secure and privacy-preserving framework for image-based IoT malware detection and prevention that couples federated learning with blockchain and honeypot-assisted behavioral monitoring, targeting Linux-capable IoT gateway devices. Portable Executable (PE) binaries are transformed into grayscale images using a corrected fixed-width byte-mapping pipeline stabilized by an information-maximizing GAN (IMGAN). A bi-level preprocessing pipeline applies two-sided weighted sparse representation (T-WSR) denoising—designed to selectively suppress zero-padding artifacts, high-entropy packed regions, and sparse opcode noise while preserving discriminative section-boundary texture—followed by geometric augmentation to mitigate class imbalance. Malware detection and family attribution are performed using a residual capsule-based network (RBCN) that fuses discriminative visual representations with PE-header features via concatenation, improving robustness against polymorphism and obfuscation. A formal threat model governs three adversary classes: a semi-honest aggregation server, a bounded fraction of malicious clients (up to 30%), and a passive eavesdropper. To enable collaboration without exposing raw data, clients train locally and share only MemCbar-encrypted updates; a permissioned Hyperledger Fabric blockchain ledger records hashed updates and security events to provide integrity, traceability, and tamper resistance. A file-system-integrated honeypot captures evasive behaviors and logs auditable evidence to strengthen prevention. Experiments on the Malimg dataset across five ablation configurations demonstrate that the corrected RBCN pipeline achieves accuracy, precision, recall, F-measure, MCC of , and AUC of in its centralized configuration, and accuracy with AUC of in the full federated configuration with five clients and eight communication rounds, substantially outperforming all baselines across all reported metrics.Open AccessA Blockchain and Federated Learning Framework for Image-Based IoT Malware Detection and Prevention
Internet of Things (IoT) devices are increasingly targeted by rapidly evolving malware, yet collaborative detection remains challenged by privacy leakage, noisy and imbalanced training data, and weak integrity guarantees when sharing model updates. This paper presents Mal-Fedchain, a secure and privacy-preserving framework for image-based IoT malware detection and prevention that couples federated learning with blockchain and honeypot-assisted behavioral monitoring, targeting Linux-capable IoT gateway devices. Portable Executable (PE) binaries are transformed into grayscale images using a corrected fixed-width byte-mapping pipeline stabilized by an information-maximizing GAN (IMGAN). A bi-level preprocessing pipeline applies two-sided weighted sparse representation (T-WSR) denoising—designed to selectively suppress zero-padding artifacts, high-entropy packed regions, and sparse opcode noise while preserving discriminative section-boundary texture—followed by geometric augmentation to mitigate class imbalance. Malware detection and family attribution are performed using a residual capsule-based network (RBCN) that fuses discriminative visual representations with PE-header features via concatenation, improving robustness against polymorphism and obfuscation. A formal threat model governs three adversary classes: a semi-honest aggregation server, a bounded fraction of malicious clients (up to 30%), and a passive eavesdropper. To enable collaboration without exposing raw data, clients train locally and share only MemCbar-encrypted updates; a permissioned Hyperledger Fabric blockchain ledger records hashed updates and security events to provide integrity, traceability, and tamper resistance. A file-system-integrated honeypot captures evasive behaviors and logs auditable evidence to strengthen prevention. Experiments on the Malimg dataset across five ablation configurations demonstrate that the corrected RBCN pipeline achieves accuracy, precision, recall, F-measure, MCC of , and AUC of in its centralized configuration, and accuracy with AUC of in the full federated configuration with five clients and eight communication rounds, substantially outperforming all baselines across all reported metrics. - Open Access
- 2 May 2026
- Published by MDPI AG in Computation
- Vol. 14 (5) , 105
Risk-Aware Downlink Throughput Prediction in High-Density 5G Networks
Accurate short-horizon downlink throughput prediction is essential for automation in high-density 5G deployments (e.g., stadiums and events), where user load, scheduling decisions, and interference conditions change rapidly and produce highly variable user-perceived rates. This paper benchmarks lightweight regression models for per-user throughput prediction from readily available radio access network (RAN) key performance indicators (KPIs) and studies a risk-aware extension that augments point forecasts with calibrated uncertainty and an abstention (deferral) rule. Experiments use a strictly time-ordered train/calibration/test protocol on the Liverpool 5G High-Density Demand (L5GHDD) dataset. The target is strongly zero-inflated (about 62% of samples at 0 Mbps) and heavy-tailed, creating regimes where average-error optimization can mask rare but operationally important bursts. In the point-prediction benchmark, the best model is a tuned two-stage support vector regressor with a mean absolute error (MAE) of Mbps, while the strongest single-stage model attains a weighted mean absolute percentage error (WMAPE) of . For uncertainty quantification, we compare standard split conformal prediction against two input-adaptive alternatives. Constant-width split conformal attains marginal coverage for a nominal 90% target with an average interval width of Mbps, but width-based deferral is degenerate because all intervals have the same size. Variable-length conformal intervals preserve near-nominal coverage () while producing informative width variation: normalized conformal reduces the average width to Mbps, and conformalized quantile regression reduces it to Mbps. At a deferral threshold of Mbps, constant-width conformal defers all samples, whereas normalized conformal still acts on of samples with selective MAE Mbps. These results show that input-adaptive uncertainty is necessary for meaningful selective prediction in heteroscedastic 5G throughput dynamics.- 2 May 2026
- Published by MDPI AG in Computation
- Vol. 14 (5) , 105
Open AccessRisk-Aware Downlink Throughput Prediction in High-Density 5G Networks
Accurate short-horizon downlink throughput prediction is essential for automation in high-density 5G deployments (e.g., stadiums and events), where user load, scheduling decisions, and interference conditions change rapidly and produce highly variable user-perceived rates. This paper benchmarks lightweight regression models for per-user throughput prediction from readily available radio access network (RAN) key performance indicators (KPIs) and studies a risk-aware extension that augments point forecasts with calibrated uncertainty and an abstention (deferral) rule. Experiments use a strictly time-ordered train/calibration/test protocol on the Liverpool 5G High-Density Demand (L5GHDD) dataset. The target is strongly zero-inflated (about 62% of samples at 0 Mbps) and heavy-tailed, creating regimes where average-error optimization can mask rare but operationally important bursts. In the point-prediction benchmark, the best model is a tuned two-stage support vector regressor with a mean absolute error (MAE) of Mbps, while the strongest single-stage model attains a weighted mean absolute percentage error (WMAPE) of . For uncertainty quantification, we compare standard split conformal prediction against two input-adaptive alternatives. Constant-width split conformal attains marginal coverage for a nominal 90% target with an average interval width of Mbps, but width-based deferral is degenerate because all intervals have the same size. Variable-length conformal intervals preserve near-nominal coverage () while producing informative width variation: normalized conformal reduces the average width to Mbps, and conformalized quantile regression reduces it to Mbps. At a deferral threshold of Mbps, constant-width conformal defers all samples, whereas normalized conformal still acts on of samples with selective MAE Mbps. These results show that input-adaptive uncertainty is necessary for meaningful selective prediction in heteroscedastic 5G throughput dynamics. - Open Access
Hybrid Sine–Cosine with Hummingbird Foraging Algorithm for Engineering Design Optimisation
We introduce AHA–SCA, a compact hybrid optimiser that alternates the wave-based exploration of the Sine–Cosine Algorithm (SCA) with the exploitation skills of the Artificial Hummingbird Algorithm (AHA) within a single population. Even iterations perform SCA moves with a linearly decaying sinusoidal amplitude to explore widely around the current best solution, while odd iterations invoke guided and territorial hummingbird flights using axial, diagonal, and omnidirectional patterns to intensify the search in promising regions. This simple interleaving yields an explicit and tunable balance between exploration and exploitation and incurs negligible overhead beyond evaluating candidate solutions. The proposed approach is evaluated on the CEC2014, CEC2017, and CEC2022 benchmark suites and on several constrained engineering design problems, including welded beam, pressure vessel, tension/compression spring, speed reducer, and cantilever beam designs. Across these diverse tasks, AHA–SCA demonstrates competitive or superior performance relative to stand-alone SCA, AHA, and a broad panel of recent metaheuristics, delivering faster early-phase convergence and robust final solutions. Statistical analyses using non-parametric tests confirm that improvements are significant on many functions, and the method respects problem constraints without parameter tuning. The results suggest that alternating wave-driven exploration with hummingbird-inspired refinement is a promising general strategy for continuous engineering optimisation.
Open AccessHybrid Sine–Cosine with Hummingbird Foraging Algorithm for Engineering Design Optimisation
We introduce AHA–SCA, a compact hybrid optimiser that alternates the wave-based exploration of the Sine–Cosine Algorithm (SCA) with the exploitation skills of the Artificial Hummingbird Algorithm (AHA) within a single population. Even iterations perform SCA moves with a linearly decaying sinusoidal amplitude to explore widely around the current best solution, while odd iterations invoke guided and territorial hummingbird flights using axial, diagonal, and omnidirectional patterns to intensify the search in promising regions. This simple interleaving yields an explicit and tunable balance between exploration and exploitation and incurs negligible overhead beyond evaluating candidate solutions. The proposed approach is evaluated on the CEC2014, CEC2017, and CEC2022 benchmark suites and on several constrained engineering design problems, including welded beam, pressure vessel, tension/compression spring, speed reducer, and cantilever beam designs. Across these diverse tasks, AHA–SCA demonstrates competitive or superior performance relative to stand-alone SCA, AHA, and a broad panel of recent metaheuristics, delivering faster early-phase convergence and robust final solutions. Statistical analyses using non-parametric tests confirm that improvements are significant on many functions, and the method respects problem constraints without parameter tuning. The results suggest that alternating wave-driven exploration with hummingbird-inspired refinement is a promising general strategy for continuous engineering optimisation.
- Open Access
- 4 January 2026
- Published by MDPI AG in Computation
- Vol. 14 (1) , 11
A Hybrid Gradient-Based Optimiser for Solving Complex Engineering Design Problems
This paper proposes JADEGBO, a hybrid gradient-based metaheuristic for solving complex single- and multi-constraint engineering design problems as well as cost-sensitive security optimisation tasks. The method combines Adaptive Differential Evolution with Optional External Archive (JADE), which provides self-adaptive exploration through p-best mutation, an external archive, and success-based parameter learning, with the Gradient-Based Optimiser (GBO), which contributes Newton-inspired gradient search rules and a local escaping operator. In the proposed scheme, JADE is first employed to discover promising regions of the search space, after which GBO performs an intensified local refinement of the best individuals inherited from JADE. The performance of JADEGBO is assessed on the CEC2017 single-objective benchmark suite and compared against a broad set of classical and recent metaheuristics. Statistical indicators, convergence curves, box plots, histograms, sensitivity analyses, and scatter plots show that the hybrid typically attains the best or near-best mean fitness, exhibits low run-to-run variance, and maintains a favourable balance between exploration and exploitation across rotated, shifted, and composite landscapes. To demonstrate practical relevance, JADEGBO is further applied to the following four well-known constrained engineering design problems: welded beam, pressure vessel, speed reducer, and three-bar truss design. The algorithm consistently produces feasible high-quality designs and closely matches or improves upon the best reported results while keeping computation time competitive.
- 4 January 2026
- Published by MDPI AG in Computation
- Vol. 14 (1) , 11
Open AccessA Hybrid Gradient-Based Optimiser for Solving Complex Engineering Design Problems
This paper proposes JADEGBO, a hybrid gradient-based metaheuristic for solving complex single- and multi-constraint engineering design problems as well as cost-sensitive security optimisation tasks. The method combines Adaptive Differential Evolution with Optional External Archive (JADE), which provides self-adaptive exploration through p-best mutation, an external archive, and success-based parameter learning, with the Gradient-Based Optimiser (GBO), which contributes Newton-inspired gradient search rules and a local escaping operator. In the proposed scheme, JADE is first employed to discover promising regions of the search space, after which GBO performs an intensified local refinement of the best individuals inherited from JADE. The performance of JADEGBO is assessed on the CEC2017 single-objective benchmark suite and compared against a broad set of classical and recent metaheuristics. Statistical indicators, convergence curves, box plots, histograms, sensitivity analyses, and scatter plots show that the hybrid typically attains the best or near-best mean fitness, exhibits low run-to-run variance, and maintains a favourable balance between exploration and exploitation across rotated, shifted, and composite landscapes. To demonstrate practical relevance, JADEGBO is further applied to the following four well-known constrained engineering design problems: welded beam, pressure vessel, speed reducer, and three-bar truss design. The algorithm consistently produces feasible high-quality designs and closely matches or improves upon the best reported results while keeping computation time competitive.
- Open Access
- 20 December 2025
- Published by MDPI AG in Computation
- Vol. 14 (1) , 1
Enhanced Chimp Algorithm and Its Application in Optimizing Real-World Data and Engineering Design Problems
This work proposes an Enhanced Chimp Optimization Algorithm (EChOA) for solving continuous and constrained data science and engineering optimization problems. The EChOA integrates a self-adaptive DE/current-to-pbest/1 (with jDE-style parameter control) variation stage with the canonical four-leader ChOA guidance and augments the search with three lightweight modules: (i) L’evy flight refinement around the incumbent best, (ii) periodic elite opposition-based learning, and (iii) stagnation-aware partial restarts. The EChOA is compared with more than 35 optimizers on the CEC2022 single-objective suite (12 functions). The results shows that the EChOA attains state-of-the-art results at both and . At , it ranks first on all functions (average rank ; wins) with the lowest mean objective and the smallest dispersion relative to the strongest competitor (OMA). At , the EChOA retains the best overall rank and achieves top scores on most functions, indicating stable scalability with problem dimension. Pairwise Wilcoxon signed-rank tests () against the full competitor set corroborate statistical superiority on the majority of functions at both dimensions, aligning with the aggregate rank outcomes. Population size studies indicate that larger populations primarily enhance reliability and time to improvement while yielding similar terminal accuracy under a fixed iteration budget. Four constrained engineering case studies (including welded beam, helical spring, pressure vessel, and cantilever stepped beam) further confirm practical effectiveness, with consistently low cost/weight/volume and tight dispersion.- 20 December 2025
- Published by MDPI AG in Computation
- Vol. 14 (1) , 1
Open AccessEnhanced Chimp Algorithm and Its Application in Optimizing Real-World Data and Engineering Design Problems
This work proposes an Enhanced Chimp Optimization Algorithm (EChOA) for solving continuous and constrained data science and engineering optimization problems. The EChOA integrates a self-adaptive DE/current-to-pbest/1 (with jDE-style parameter control) variation stage with the canonical four-leader ChOA guidance and augments the search with three lightweight modules: (i) L’evy flight refinement around the incumbent best, (ii) periodic elite opposition-based learning, and (iii) stagnation-aware partial restarts. The EChOA is compared with more than 35 optimizers on the CEC2022 single-objective suite (12 functions). The results shows that the EChOA attains state-of-the-art results at both and . At , it ranks first on all functions (average rank ; wins) with the lowest mean objective and the smallest dispersion relative to the strongest competitor (OMA). At , the EChOA retains the best overall rank and achieves top scores on most functions, indicating stable scalability with problem dimension. Pairwise Wilcoxon signed-rank tests () against the full competitor set corroborate statistical superiority on the majority of functions at both dimensions, aligning with the aggregate rank outcomes. Population size studies indicate that larger populations primarily enhance reliability and time to improvement while yielding similar terminal accuracy under a fixed iteration budget. Four constrained engineering case studies (including welded beam, helical spring, pressure vessel, and cantilever stepped beam) further confirm practical effectiveness, with consistently low cost/weight/volume and tight dispersion. - Open Access
Enhanced Henry Gas Solubility Optimization for Solving Data and Engineering Design Problems
Many engineering design problems are formulated as constrained optimization tasks that are nonlinear and nonconvex, and often treated as black boxes. In such cases, metaheuristic algorithms are attractive because they can search complex design spaces without requiring gradient information. In this work, we propose an Enhanced Henry Gas Solubility Optimization (eHGSO) algorithm, which is an improved version of the physics-inspired HGSO method. The enhanced variant introduces six main contributions: (i) a more diverse, population-wide initialization strategy to cover the design space more thoroughly; (ii) adaptive temperature/pressure control parameters that automatically shift the search from global exploration to local refinement; (iii) an elitist archive with differential perturbation that accelerates exploitation around high-quality candidate designs; (iv) a simple combination of the global HGSO search moves with a lightweight gradient-free local search to refine promising solutions; (v) a constraint-handling mechanism that explicitly prioritizes feasible solutions while still allowing exploration near the constraint boundaries; and (vi) a complexity and ablation analysis that quantifies the impact of each mechanism and confirms that they introduce only modest computational overhead. We evaluate eHGSO on four classical constrained engineering design problems: the stepped cantilever beam, the tension/compression spring, the welded beam, and the three-bar truss. Its performance is compared with seventeen recent metaheuristic optimizers over multiple independent runs. eHGSO achieves the best average objective value on the cantilever, spring, and welded-beam problems and shares the best average result on the three-bar truss. Compared to the second-best method, the mean objective is improved by about for the cantilever beam and for the welded beam, while the spring and truss results are essentially equivalent at four significant figures. Convergence and robustness analyses show that eHGSO reaches high-quality solutions quickly and consistently. Overall, the proposed eHGSO algorithm appears to be a competitive and practical tool for constrained engineering design problems.Open AccessEnhanced Henry Gas Solubility Optimization for Solving Data and Engineering Design Problems
Many engineering design problems are formulated as constrained optimization tasks that are nonlinear and nonconvex, and often treated as black boxes. In such cases, metaheuristic algorithms are attractive because they can search complex design spaces without requiring gradient information. In this work, we propose an Enhanced Henry Gas Solubility Optimization (eHGSO) algorithm, which is an improved version of the physics-inspired HGSO method. The enhanced variant introduces six main contributions: (i) a more diverse, population-wide initialization strategy to cover the design space more thoroughly; (ii) adaptive temperature/pressure control parameters that automatically shift the search from global exploration to local refinement; (iii) an elitist archive with differential perturbation that accelerates exploitation around high-quality candidate designs; (iv) a simple combination of the global HGSO search moves with a lightweight gradient-free local search to refine promising solutions; (v) a constraint-handling mechanism that explicitly prioritizes feasible solutions while still allowing exploration near the constraint boundaries; and (vi) a complexity and ablation analysis that quantifies the impact of each mechanism and confirms that they introduce only modest computational overhead. We evaluate eHGSO on four classical constrained engineering design problems: the stepped cantilever beam, the tension/compression spring, the welded beam, and the three-bar truss. Its performance is compared with seventeen recent metaheuristic optimizers over multiple independent runs. eHGSO achieves the best average objective value on the cantilever, spring, and welded-beam problems and shares the best average result on the three-bar truss. Compared to the second-best method, the mean objective is improved by about for the cantilever beam and for the welded beam, while the spring and truss results are essentially equivalent at four significant figures. Convergence and robustness analyses show that eHGSO reaches high-quality solutions quickly and consistently. Overall, the proposed eHGSO algorithm appears to be a competitive and practical tool for constrained engineering design problems. - Open Access
Ripple Evolution Optimizer: A Novel Nature-Inspired Metaheuristic
This paper presents a novel Ripple Evolution Optimizer (REO) that incorporates adaptive and diversified movement—a population-based metaheuristic that turns a coastal-dynamics metaphor into principled search operators. REO augments a JADE-style current-to-p-best/1 core with jDE self-adaptation and three complementary motions: (i) a rank-aware that pulls candidates toward the best, (ii) a time-increasing that aligns agents with an elite mean, and (iii) a scale-aware sinusoidal that lead solutions with a decaying envelope; rare Lévy-flight kicks enable long escapes. A reflection/clamp rule preserves step direction while enforcing bound feasibility. On the CEC2022 single-objective suite (12 functions spanning unimodal, rotated multimodal, hybrid, and composition categories), REO attains 10 wins and 2 ties, never ranking below first among 34 state-of-the-art compared optimizers, with rapid early descent and stable late refinement. Population-size studies reveal predictable robustness gains for larger N. On constrained engineering designs, REO achieves outperforming results on Welded Beam, Spring Design, Three-Bar Truss, Cantilever Stepped Beam, and 10-Bar Planar Truss. Altogether, REO couples adaptive guidance with diversified perturbations in a compact, transparent optimizer that is competitive on rugged benchmarks and transfers effectively to real engineering problems.
Open AccessRipple Evolution Optimizer: A Novel Nature-Inspired Metaheuristic
This paper presents a novel Ripple Evolution Optimizer (REO) that incorporates adaptive and diversified movement—a population-based metaheuristic that turns a coastal-dynamics metaphor into principled search operators. REO augments a JADE-style current-to-p-best/1 core with jDE self-adaptation and three complementary motions: (i) a rank-aware that pulls candidates toward the best, (ii) a time-increasing that aligns agents with an elite mean, and (iii) a scale-aware sinusoidal that lead solutions with a decaying envelope; rare Lévy-flight kicks enable long escapes. A reflection/clamp rule preserves step direction while enforcing bound feasibility. On the CEC2022 single-objective suite (12 functions spanning unimodal, rotated multimodal, hybrid, and composition categories), REO attains 10 wins and 2 ties, never ranking below first among 34 state-of-the-art compared optimizers, with rapid early descent and stable late refinement. Population-size studies reveal predictable robustness gains for larger N. On constrained engineering designs, REO achieves outperforming results on Welded Beam, Spring Design, Three-Bar Truss, Cantilever Stepped Beam, and 10-Bar Planar Truss. Altogether, REO couples adaptive guidance with diversified perturbations in a compact, transparent optimizer that is competitive on rugged benchmarks and transfers effectively to real engineering problems.