Nikunj Mashru

This study introduces Multi-Objective Chaos Game Optimization (MOCGO), a metaheuristic algorithm developed for the purpose of handling multi-objective structural optimization problems with constraints. In doing so, MOCGO utilizes a single-objective algorithm known as Chaos Game Optimization (CGO) in conjunction with chaos exploration dynamics, a diversity-preserving archive, and a grid-selection process to create an effective set of Pareto optimal solutions. This algorithm has been tested on six different trusses (10-bar to 120-bar) based on the problem of minimizing the weight and compliance of a structure while satisfying the stress constraint criteria. As measures of fitness, MOCGO performs significantly better than other algorithms, including NSGA-II, MOGOA, MOALO, and MOAVOA. These results demonstrate MOCGO as an efficient and robust approach for constrained multi-objective structural design.

This study evaluates the performance of a multi-walled carbon nanotube (MWCNT)-based nanofluid as an alternative cutting fluid in the turning of EN31 steel. Experiments were conducted under dry, conventional coolant, and nanofluid-assisted conditions using a gravity-feed lubrication system with a flow rate of 1 L/h. The results indicate that the nanofluid improves machining performance, with reductions of 5–20% in cutting force, 11–37% in thrust force, 4–33% in feed force, and 2–34% in surface roughness compared to conventional cooling. Response Surface Methodology (RSM) with Central Composite Design was applied to develop predictive models for surface roughness and resultant cutting force. The models showed good statistical significance and prediction accuracy. Multi-objective optimization was performed to minimize surface roughness and cutting force while maximizing material removal rate. The optimal parameters were identified as 1500 rpm cutting speed, 0.10 mm/rev feed rate, and 1 mm depth of cut, resulting in surface roughness of approximately 0.47 μm, cutting force of 165 N, and material removal rate of 7.54 mm³/min. Comparative analysis of optimization algorithms showed that NSGA-II provided superior convergence and stability, achieving better performance than other methods. The findings confirm the effectiveness of nanofluid-assisted machining combined with intelligent optimization for improved machining outcomes.

Metaheuristic algorithms have now become critical in solving nonlinear and constrained engineering design problems that are hard to solve by traditional optimization methods. Quadratic interpolation optimization (QIO) is a new technique that is founded on interpolation principles, yet its effectiveness is frequently restricted by an early convergence and a lack of local refinement when used on real-life problems. A better version of this is proposed in this research and is known as Adaptive quadratic interpolation optimization (AQIO). AQIO complements QIO in three ways: adaptive parameter adjustment to achieve a smooth trade-off between exploration and exploitation, elite dynamic opposition (EDO) to preserve the diversity of the population, and cauchy perturbation to reinforce local exploitation. Six popular engineering design problems, such as spring design, pressure vessel design, welded beam design, speed reducer design, gear train design, and three-bar truss optimization, are used to test the performance of AQIO. Comparative findings indicate that AQIO is always better than the original QIO and some of the known optimization algorithms in terms of quality of solution, speed of convergence, and strength. These results show that AQIO is an effective and trustworthy optimization method to solve constrained engineering design problems.

This paper presents a new multi-objective optimization algorithm called the Multi-Objective Crested Porcupines Optimization (MOCPO) Algorithm, which uses an elitist, non-dominated sorting and crowding distance mechanism. MOCPO is motivated by the predator-prey behavior of crested porcupines and is based on the newly proposed Crested Porcupines Algorithm. MOCPO is formulated to efficiently manage conflicting objectives in multi-objective optimization problems. Through the use of non-dominated sorting and crowding distance mechanisms, MOCPO promotes solution diversity and convergence towards the Pareto front. MOCPO employs a new Information Feedback Mechanism (IFM) and an enhanced solution updating strategy to enhance convergence and diversity control. The performance of MOCPO is tested on a variety of benchmark problems, including the ZDT and DTLZ series, as well as real-world engineering design problems from the RWMOP suite. These test problems represent a variety of optimization problems with linear, nonlinear, continuous, and discrete nature. MOCPO performance is compared with state-of-the-art algorithms like the Multi-Objective Gradient Based Optimizer (MOGBO), Preference inspired Differential Evolution (Pre-DEMO), Multi-Objective Exponential Distribution Algorithm (MOEDO), Pivot solution based Multi-Objective Evolutionary Algorithm (Pi-MOEA), and Clustering aided Grid based Multi-Objective Evolutionary Algorithm (ClGrMOEA). Qualitative and quantitative analyses using standard performance metrics show the effectiveness of the algorithm. Experimental results verify that MOCPO provides substantial improvements in convergence and solution diversity, making it a viable choice for solving complex multi-objective optimization problems.

This research presents an advancement of the Elk Herd Optimization targeting specific real-world multi-objective optimization problems, this algorithm is stated as the multi-objective Elk Herd Optimization (MOEHO). MOEHO exploits reproductive behaviour among elk herds for balancing exploration and exploitation within the optimization procedure toward diversification and convergence. The algorithm performed better over the set of small-to-medium scale structural design problems thus is widely applicable in engineering design. Further, when compared with eight benchmark truss structures against five well-established algorithms the MOEHO has outperformed them in the perspective of performance parameters like Spacing (SP), Hypervolume (HV) and Inverted Generational Distance (IGD). More concrete statistical analysis through Friedman rank test also ascertains the robustness and efficiency of the algorithm, especially at high complexities in optimization. The research attracts attention to the ability of such an algorithm which maintains a balance between the exploration and exploitation. Computational efficiency of MOEHO and qualitatively diversifying solutions along Pareto front, makes it especially applicable in complex engineering applications. Further research into extension of MOEHO with applicability on more dimensional problems, applied even in energy systems optimization.

The study suggests a better multi-objective optimization method called 2-Archive Multi-Objective Cuckoo Search (MOCS2arc). It is then used to improve eight classical truss structures and six ZDT test functions. The optimization aims to minimize both mass and compliance simultaneously. MOCS2arc is an advanced version of the traditional Multi-Objective Cuckoo Search (MOCS) algorithm, enhanced through a dual archive strategy that significantly improves solution diversity and optimization performance. To evaluate the effectiveness of MOCS2arc, we conducted extensive comparisons with several established multi-objective optimization algorithms: MOSCA, MODA, MOWHO, MOMFO, MOMPA, NSGA-II, DEMO, and MOCS. Such a comparison has been made with various performance metrics to compare and benchmark the efficacy of the proposed algorithm. These metrics comprehensively assess the algorithms' abilities to generate diverse and optimal solutions. The statistical results demonstrate the superior performance of MOCS2arc, evidenced by enhanced diversity and optimal solutions. Additionally, Friedman's test & Wilcoxon’s test corroborate the finding that MOCS2arc consistently delivers superior optimization results compared to others. The results show that MOCS2arc is a highly effective improved algorithm for multi-objective truss structure optimization, offering significant and promising improvements over existing methods.