A general hybrid framework for many-objective optimization: integrating local search into reference-vector-based evolutionary algorithms

A new dual-population constrained multi-objective evolutionary optimization algorithm with repair constraint handling for structural optimization

This study introduces a novel constrained multi-objective evolutionary algorithm, termed DPCME, which employs two interacting populations that exchange information, enabling effective global exploration and reducing the risk of convergence to local optima.

An Evolutionary Algorithm Based on Dynamic Grid Search for Constrained Multimodal Multiobjective Optimization

Constrained multimodal multiobjective optimization problems (CM-MOPs) widely exist in real-world applications and are characterized by the coexistence of constraints and multimodality. Solving CMMOPs requires identifying multiple feasible Pareto-optimal solutions with identical objective values. However, many existing algorithms tend to converge prematurely to local feasible regions and fail to discover all equivalent Pareto-optimal solutions. To address this issue, this paper proposes a dynamic grid search-based evolutionary algorithm (DGSEA) for CMMOPs. DGSEA assigns a dynamic grid space to each solution, which expands as the evolution progresses. In the early stage, a small grid promotes effective exploration of discrete feasible regions while maintaining a well-distributed set of candidate solutions. In the middle and later stages, the expanded grid helps eliminate redundant solutions and achieves a better balance among feasibility, convergence, and diversity. Moreover, a grid-based density metric is incorporated into mating and environmental selection to generate and select offspring with good distribution. Experimental comparisons with state-of-the-art algorithms demonstrate that DGSEA achieves superior performance in solving CMMOPs.

An Evolution Algorithm with Objective-Wise Variable Analysis for Sparse Large-Scale Multi-Objective Optimization

An objective-wise variable analysis method that first evaluates the sensitivity of each objective to all decision variables, and then comprehensively aggregates the sensitivity information across multiple objectives to estimate the overall importance of decision variables is proposed.

An Adaptive PSO–GA Hybrid Optimization with Diversity-Aware and Stagnation-Resistant Mathematical Framework for SVR

Support Vector Regression (SVR) is a reliable and generalizable model for non-linear regression, but its performance depends strongly on selecting proper hyperparameters such as the penalty parameter $C$, kernel width $\gamma$, and the $\epsilon$-insensitive loss. Because the SVR objective is highly nonconvex with a multimodal error surface, manual tuning and grid search become inefficient. Metaheuristic optimizers therefore offer a more practical alternative through population-based search. Although Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) have both shown promise, each has clear limitations: PSO converges quickly but often stagnates in local optima due to loss of diversity, whereas GA preserves diversity but converges slowly because of excessive random exploration. To address these complementary weaknesses, this work introduces a mathematically formulated hybrid framework called Adaptive PSO-GA (A-PSO-GA). Experiments on the Housing, Concrete, and Abalone benchmarks show that A-PSO-GA consistently outperforms PSO-only, GA-only, and non-adaptive hybrid baselines in RMSE, MAE, MAPE, and $R^{2}$. It also achieves faster and more stable convergence across multiple seeds, demonstrating improved prediction accuracy, robustness, and optimization reliability. These results confirm that mathematically justified adaptive mechanisms are important for effective evolutionary hyperparameter tuning of SVR.

A dynamic dual-population co-evolutionary algorithm for constrained multi-objective optimization problems

A coordinated hybrid initialization and multi-operator framework for swarm-based optimization

Population-based metaheuristic algorithms are widely applied to both continuous and discrete optimization problems; however, their performance is often constrained by limitations in population initialization quality and the coordination between exploration and exploitation, particularly in complex and high-dimensional optimization landscapes. In many existing approaches, uniform random initialization leads to uneven spatial coverage in continuous domains and insufficient solution diversity, while single-mechanism search strategies often fail to provide an effective transition between global exploration and local exploitation. These structural limitations reduce search robustness and increase the risk of premature convergence, especially in multimodal, hybrid, and composition optimization problems. To address these challenges, this study proposes a coordinated enhancement framework that jointly improves population initialization and search coordination. The proposed framework integrates a hybrid dual-population initialization strategy with a threshold-based multi-operator search mechanism. During initialization, uniform random sampling and hyperbolic transformation are combined with superiority-based selection to generate a high-quality and diverse initial population. Throughout the optimization process, cosine contraction, sinusoidal perturbation operator, and the original SCSA search operator are coordinated using an iteration-regulated search-strength coefficient with predefined threshold intervals, enabling a structured transition between exploration and exploitation. Based on this framework, an enhanced sand cat swarm algorithm (ESC-SCSA) is developed and comprehensively evaluated through sensitivity analysis, ablation studies, convergence analysis, and statistical significance tests on the CEC2017 and CEC2022 benchmark suites, followed by validation on flexible job shop scheduling problem instances. Experimental results demonstrate that ESC-SCSA consistently improves the original SCSA and exhibits competitive performance compared with several state-of-the-art metaheuristic algorithms across the CEC2017 and CEC2022 benchmark suites. Sensitivity analysis identifies effective parameter settings for the proposed framework, while the ablation study confirms that the hybrid initialization strategy and threshold-based multi-operator coordination each contribute substantially to the observed performance improvements. Convergence analysis further reveals faster and more stable search behaviour throughout the optimization process. Moreover, ESC-SCSA achieved the lowest mean makespan on nine of the ten Brandimarte benchmark instances, indicating its effectiveness and potential generalization capability across both continuous and discrete optimization problems.