Differential evolution with fitness-based mutation strategy and diversity enhancement mechanism for numerical optimization

An Improved Differential Evolution Algorithm Based on the Biological Characteristics of the Squirting Cucumber

Differential evolution (DE) is widely used in numerical optimization, but fixed parameters and a single mutation strategy make it difficult to coordinate exploration and exploitation across different search stages. To address this issue, this paper proposes Bio-EbDE, a DE algorithm with stage-aware mutation strategy sw...

A Hybrid Particle Swarm Optimization and Differential Evolution Algorithm with Adaptive Population and Dynamic Parameter Allocation

Traditional particle swarm optimization (PSO) easily falls into premature convergence, while differential evolution (DE) is highly sensitive to fixed control parameters. Existing PSO-DE hybrid frameworks suffer from static population sizes and insufficient cross-population information exchange. This paper proposes PSO-...

An Improved Attraction-Repulsion OptimizationAlgorithm for Global Optimization and EngineeringDesign Problems

Attraction-Repulsion Optimization Algorithm (AROA) is a recently proposed meta-heuristic algorithm known for its simplicity, ease of implementation, and robustness. However, AROA may converge to local optima when applied to complex optimization problems. To address this limitation, we propose an enhanced version called...

Evolutionary polar fox optimization algorithm: an enhanced metaheuristic for feature selection

A dynamic multi-objective optimization evolutionary algorithm based on hybrid initialization and ensemble model prediction strategy

The experimental results show that the proposed DMOEA based on innovative hybrid initialization and ensemble prediction strategy, coupled with dynamic mutation adjustment not only responds quickly to dynamic environmental changes, but also achieves outstanding results in convergence and diversity.

A hybrid optimization algorithm of differential evolution and sine cosine algorithm

The results demonstrate that DESCA is an effective and competitive optimization method with favorable convergence behavior, high-quality solutions, and strong engineering applicability.