Xinyu Cai

Tax-benefit policy acts most directly on whether adults work. We show that the response-threshold rule by which social insects divide labour and the extensive-margin participation curve share one functional form when the private cost of working is additive and log-logistic; an assumed participation elasticity then implies the exponent. The correspondence defines a tax-benefit design problem with three objectives—participation, welfare and fiscal cost–whose exact supported Pareto front we compute, and an eleven-instrument version for single adults and lone parents. It also drives MOEA/D-DoL, a decomposition-based algorithm whose subproblems are served by threshold-driven workers that compare their success with the colony’s. By IGD, MOEA/D-DoL beats its MOEA/D-DE core clearly on two of seventeen benchmarks, both used in its design, and on two of nine held-out WFG problems, losing on one benchmark; AR-MOEA, NSGA-III and NSGA-II rank higher on both suites. The schedules that raise participation most at no extra cost lower the guaranteed income and pay an in-work credit financed by a higher tax rate. This direction survives joint calibration draws, a 100% cap on marginal rates and wider instrument ranges, but the tax is cheap partly because hours do not respond, and the gain varies in size.

Improvements to bio-inspired optimisers usually answer generic defects with generic operators. This paper instruments the red-billed blue magpie optimizer (RBMO) and measures what fails. Both operators scale their step by differences between population positions, and the attack places every candidate at the incumbent best, so the search scale shrinks with the spread the attack destroys. On five instrumented functions of the IEEE Congress on Evolutionary Computation 2017 (CEC2017) benchmark suite the population collapses to numerical identity, and on four the error stagnates over the last 28% to 58% of the run. The proposed collapse-resistant RBMO (CRBMO) breaks the loop with a dual-anchor attack, a radius control under the one-fifth success rule and an adaptive foraging allocation; it adds no chaotic map, Levy flight or opposition-based learning. On CEC2017 at 30,000 evaluations, against nine swarm algorithms, it attains the best Friedman rank at D = 30, 50 and 100 and 1.72 overall against 3.16 for RBMO. It outranks three published RBMO variants in every dimension, but the covariance matrix adaptation evolution strategy (CMA-ES) and the single-objective differential evolution algorithm jSO outrank it overall. At the standard CEC2017 budget its lead over RBMO is not significant; success-history-based adaptive differential evolution with linear population size reduction (L-SHADE), jSO and CMA-ES outrank it. Applied to programme targeting and retraining pathway design in employment policy, CRBMO beats RBMO in every instance, though the crested porcupine optimizer (CPO) ranks first.

The time cost of care constrains female labour supply; two policy levers act on it: where childcare capacity is sited and how shifts are scheduled, both large combinatorial problems. The crested porcupine optimizer (CPO) initialises at random, guides exploitation by a single random or best individual and resizes its population on a fixed cycle. The proposed multi-strategy CPO (MSCPO) keeps CPO’s four defence operators and replaces those original components with Sobol quasi-opposition initialisation, elite-guided exploitation and monotone fitness-based population reduction. On the 29 CEC2017 functions at 10, 30, 50 and 100 dimensions (30 runs of 30,000 evaluations), MSCPO had the best Friedman mean rank among ten algorithms at every dimension (1.29 against 2.53 for CPO; χ²(9) = 878.5, p < 0.001), and pooled Holm tests separated it from all nine competitors (p ≤ 0.002). A factorial ablation attributed about 70% of the gain to elite-guided exploitation. The advantage over CPO vanished at the full budget of 10⁴D evaluations. In childcare siting, MSCPO ranked first, leaving the dispersion of access 28–35% below the most equal siting rule with at least 99% of the best rule’s employment; in shift scheduling, it ranked fifth, with instrumentation suggesting a limitation of large steps under a rank-key decoder.

Career guidance usually treats a narrow option set as the problem, and career decision-making difficulties are modeled as decreasing in the range of options a young person perceives. This study asked whether that association is monotone. Three questionnaires spaced four weeks apart were completed by 1036 Chinese young adults (mean age 23.7) making a career decision: perceived career option breadth, maximizing tendency and career decision-making self-efficacy at Wave 1, anticipated career regret at Wave 2, and career decision-making difficulties at Wave 3. The data provided evidence of a U shape (quadratic b = 0.089, p < 0.001; Lind–Mehlum test p = 0.004). Difficulties reached a minimum 0.66 standard deviations above the mean (about 6.5 occupations in a parallel count-based model); the rise beyond it was less precisely estimated and sensitive to trimming and to the test used. Anticipated career regret rose convexly with option breadth, and accounting for it reduced the quadratic coefficient by 78%, while the direct association remained negative. Participants with a stronger maximizing tendency reached the estimated turning point at a lower option breadth. Career decision-making self-efficacy did not significantly weaken the regret–difficulty association. Wider option sets were associated with fewer difficulties only up to a point.

Visual art and graphic design tasks, such as generating a harmonious color palette or arranging the elements of a page, can be naturally formulated as mathematical optimization problems whose objective functions are non-convex, multimodal, and non-differentiable. Metaheuristic algorithms are well-suited to such problems. The secretary bird optimization algorithm (SBOA) is a recently proposed bio-inspired metaheuristic that mimics the hunting and predator-escaping behaviors of secretary birds, and it has shown competitive performance. However, SBOA still suffers from insufficient population diversity, premature convergence, and an unbalanced transition between exploration and exploitation, which limits its accuracy on complex design problems. To overcome these limitations, this paper proposes a multi-strategy secretary bird optimization algorithm (MSSBOA) that integrates three improvement strategies. First, a good point set initialization is employed to generate a low-discrepancy initial population that covers the search space more uniformly and enriches population diversity. Second, a lens opposition-based learning strategy is applied to the inferior individuals to help the population escape local optima while preserving the elite. Third, an adaptive Cauchy–Gaussian mutation is imposed on the best individual to balance global exploration and local exploitation throughout the search. The performance of MSSBOA is comprehensively examined on the CEC2017 benchmark suite in 10, 30, 50, and 100 dimensions and compared with the basic SBOA and nine state-of-the-art algorithms; the results are analyzed by the Friedman test, the Nemenyi post hoc test and the Wilcoxon rank-sum test. MSSBOA is then applied to two representative visual-design optimization problems: aesthetic color-harmony palette generation and graphic-layout aesthetics optimization. In all cases, MSSBOA outperforms the basic SBOA and the competing algorithms in terms of convergence speed, stability, and solution quality, confirming its effectiveness for computational-aesthetics applications in art design.

Enterprise Development Optimizer (EDO) is a meta-heuristic algorithm inspired by the enterprise development process. Although EDO is effective in the optimization field, it suffers from issues such as premature convergence and unequal exploration–exploitation ratio. These shortcomings restrict the performance of the algorithm in some complex problem. This research proposes an improved EDO, called LMEDO, in which EDO is integrated with incorporates time-phase based switching strategy, economy-driven guided based learning strategy and spatial selectivity-based selection strategy to improve convergence rate, stability, and search effectiveness. Among these strategies, the time-phase based switching strategy allows EDO to better apply different search strategies and enhances the search capability. Economy-driven guided learning-based strategy helps EDOs absorb valid information from dominant groups, which in turn improves the quality of the entire population. The spatial selectivity-based selection strategy achieves a balance between exploitation and exploration capabilities. To validate the performance of LMEDO, an extensive evaluation of the CEC 2018 test suite and five engineering optimization problems was performed. Parameter sensitivity analysis assisted LMEDO in determining the optimal parameter settings. Ablation experiments confirmed the effectiveness and compatibility of the improved strategies. The superiority of LMEDO is validated by comparing it with state-of-the-art algorithms such as LSHADE-SPACMA, APSM-jSO, and GLS-MPA. LMEDO received an average ranking of 2.5862 on the CEC2018 test suite and obtained a result of 1161/94/143 (+/=/−) on the Wilcoxon rank sum test. In addition, engineering design optimization problems are investigated to further demonstrate the reliability and flexibility of LMEDO. In conclusion, LMEDO is a promising variant of metaheuristic algorithms and is effective and accurate for solving complex problems.

In real-world applications, many complex problems can be formulated as mathematical optimization challenges, and efficiently solving these problems is critical. Metaheuristic algorithms have proven highly effective in addressing a wide range of engineering issues. The differentiated creative search is a recently proposed evolution-based meta-heuristic algorithm with certain advantages. However, it also has limitations, including weakened population diversity, reduced search efficiency, and hindrance of comprehensive exploration of the solution space. To address the shortcomings of the DCS algorithm, this paper proposes a multi-strategy differentiated creative search (MSDCS) based on the collaborative development mechanism and population evaluation strategy. First, this paper proposes a collaborative development mechanism that organically integrates the estimation distribution algorithm and DCS to compensate for the shortcomings of the DCS algorithm’s insufficient exploration ability and its tendency to fall into local optimums through the guiding effect of dominant populations, and to improve the quality of the DCS algorithm’s search efficiency and solution at the same time. Secondly, a new population evaluation strategy is proposed to realize the coordinated transition between exploitation and exploration through the comprehensive evaluation of fitness and distance. Finally, a linear population size reduction strategy is incorporated into DCS, which significantly improves the overall performance of the algorithm by maintaining a large population size at the initial stage to enhance the exploration capability and extensive search of the solution space, and then gradually decreasing the population size at the later stage to enhance the exploitation capability. A series of validations was conducted on the CEC2018 test set, and the experimental results were analyzed using the Friedman test and Wilcoxon rank sum test. The results show the superior performance of MSDCS in terms of convergence speed, stability, and global optimization. In addition, MSDCS is successfully applied to several engineering constrained optimization problems. In all cases, MSDCS outperforms the basic DCS algorithm with fast convergence and strong robustness, emphasizing its superior efficacy in practical applications.