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 switching, subpopulation-specific parameter adaptation, and a local optimum escape mechanism. Its main distinction lies in three coordinated designs: mutation strategies are selected according to both evolutionary stage and subpopulation role; the scaling factor F is generated from different distributions for different subpopulations; and a spray-melon-inspired perturbation generates offspring with adaptive dispersal when premature convergence is detected. Experiments on CEC2013, CEC2022, and a constrained engineering problem show that Bio-EbDE achieves competitive optimization accuracy and convergence behavior.

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-DE-ADP, a hybrid optimizer with sinusoidal adaptive parameters, elite-guided mutation, ring neighborhood-weighted PSO and fitness-driven dynamic dual-population allocation. Four complementary mechanisms are integrated: (i) sine-wave perturbation superimposed on linear decay adaptively adjusts PSO inertia weight, acceleration factors and DE scaling/crossover coefficients to balance search stages; (ii) global elite individuals are embedded into DE mutation to reduce blind random search; (iii) ring topology with weighted learning realizes bidirectional information interaction between PSO and DE subpopulations; (iv) the proportion of PSO/DE individuals is dynamically adjusted according to elite ratio to allocate computing resources. Experiments adopt the CEC2017 30-dimensional benchmark with 30 test functions covering unimodal, multimodal, hybrid and composite landscapes. Compared with 8 state-of-the-art metaheuristics, PSO-DE-ADP achieves the lowest Friedman rank (1.08 vs. 2.23–4.90 for PSO variants; 1.53 vs. 2.07–5.00 for non-PSO algorithms). Ablation tests prove each component significantly boosts accuracy; The algorithm only costs 0.172 s average runtime, superior to all competitors. Statistical Wilcoxon and Friedman tests verify its significant superiority. Future work extends this method to multi-objective, constrained and real engineering optimization tasks.

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 the Differential Cauchy Tangent Attraction-Repulsion Optimization Algorithm (DCTAROA). First, we propose a mutation operator based on a tangent flight mutation strategy and a dimension decision mechanism using the inverse cumulative distribution function of the Cauchy distribution. The tangent flight mutation enhances the local search capability and accelerates convergence, while the dimension decision strategy of the Cauchy distribution inverse cumulative function increases population diversity and improves exploration efficiency. Subsequently, we integrate Differential Evolution (DE) as a local search mechanism to strengthen the global optimization performance of AROA. To evaluate the proposed algorithm, we compare it with 15 state-of-the-art algorithms on 29 CEC2017 benchmark functions across various dimensions. Experimental results demonstrate that DCTAROA outperforms the compared algorithms in terms of solution accuracy, stability, convergence speed, and statistical significance based on the Wilcoxon rank-sum test. Furthermore, we apply DCTAROAto three practical engineering design problems. The results confirm that DCTAROA effectively explores the search space and yields competitive solutions, thereby validating its practical applicability.

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

A hybrid optimization algorithm of differential evolution and sine cosine algorithm

To address the inherent limitations of the sine cosine algorithm (SCA), including slow convergence, limited optimization accuracy, and a tendency to become trapped in local optima, a hybrid optimization algorithm of differential evolution and sine cosine algorithm (DESCA) is proposed. Instead of simply combining different operators, DESCA is designed as a coordinated two-phase search framework with complementary functional roles. First, identical parameter settings for r2, r3, and r4 are adopted for all dimensions of the same individual to reduce randomness and improve search efficiency. Second, the spiral update strategy from the whale optimization algorithm (WOA) is introduced as a local refinement mechanism to further improve the solutions generated by SCA and strengthen local exploitation. Third, within the two-phase framework, SCA primarily performs global exploration in the early stage, while DE with the DE/best/1 mutation strategy is activated in the later stage to improve convergence accuracy and local exploitation capability. The performance of DESCA is comprehensively evaluated on 23 classical benchmark functions, the IEEE CEC2014 benchmark functions, and the IEEE CEC2020 benchmark functions. Experimental results show that DESCA achieves strong optimization performance on the 23 classical benchmark functions and the CEC2014 benchmark functions, while maintaining competitive overall performance on the CEC2020 benchmark functions. In addition, Wilcoxon rank-sum tests and Friedman tests are conducted to further evaluate statistical significance and overall ranking performance. To verify its practical applicability, DESCA is further applied to four mechanical design optimization problems. The results demonstrate that DESCA is an effective and competitive optimization method with favorable convergence behavior, high-quality solutions, and strong engineering applicability.