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

A Hybrid DE-PSO Algorithm for Constrained Optimization Problems with Distinct Constraint Handling

Constrained optimization problems (COPs) are widely encountered in real-world applications and remain challenging to solve. Accordingly, developing efficient solution methods for COPs continues to be an important research topic. This paper proposes a novel hybrid DE-PSO algorithm, termed Dual-Population DEPSO (DP-DEPSO), to address COPs. DP-DEPSO is built upon two key ideas. First, a dual-population scheme is adopted in which Differential Evolution (DE) and Particle Swarm Optimization (PSO) evolve in parallel. Second, population-distinct constraint-handling schemes are introduced, where the DE group employs Deb's feasibility rule to preserve and explore high-quality feasible solutions, while the PSO group utilizes Deb's rule and an ε constraint handling method to exploit promising regions. Through complementary search behaviors of the dual population, DP-DEPSO achieves a well-balanced exploration-exploitation search. The proposed method is evaluated on thirteen classical constrained benchmark problems and compared with state-of-the-art evolutionary algorithms and existing DEPSO. Experimental results demonstrate that DP-DEPSO attains superior solution quality and competitive convergence efficiency. Statistical analyses using the Wilcoxon signed-rank test further confirm its significant performance advantage over most comparison methods. These results indicate that DP-DEPSO provides an effective and robust framework for COPs.

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 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.

Constrained Optimization using an Evolutionary - Augmented Lagrangian approach

This work proposes a Hybrid Augmented Lagrangian (HyAL) method that combines the constraint-handling capabilities of the AL framework with the exploratory power of population-based search, and demonstrates that HyAL effectively solves constrained optimization problems, including high-dimensional cases where population-based methods struggle.

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

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

Results show that integrating local search significantly enhances performance, while a principled method for setting hybrid parameters ensures robustness and reproducibility, highlighting the potential of combining mathematical programming techniques with evolutionary algorithms for high-dimensional many-objective optimization problems.