Jul 2026· Annual Conference on Genetic and Evolutionary Computation· pp. 1295-1303· 0 citations· 38 references
Computer Science
Abstract
Many Particle Swarm Optimization (PSO) variants often fail to approach the global optimum due to complex fitness landscapes and/or the structural rigidity of these variants. We developed a novel framework named PSO-SEM, centered on macro-evolutionary population management. PSO-SEM introduces three landscape-driven operators: Split (Topological Fission), Extinction (Density-driven Recycling), and Migration (Knowledge Transfer). These operators autonomously regulate the lifecycle of sub-swarms to intensify search in high-potential areas by identifying promising basins and recycling computational resources from stagnant regions. Experimental results show that PSO-SEM achieves a top-tier ranking and demonstrates significant competitiveness against 14 state-of-the-art algorithms. Behavioral monitoring and ablation studies confirm PSO-SEM's ability to maintain autonomous exploration/exploitation balance through landscape-based computational resource re-allocation. Our findings verify that PSO-SEM is an interpretable architecture that meets the diverse requirements of black-box optimization.
Particle Swarm Optimization (PSO) remains highly effective on many continuous optimization problems, yet its search dynamics collapse too quickly on high-dimensional and deceptive landscapes. Our earlier work introduced single-role diversity-enhancing PSO variants and showed that diversity injection is effective only when embedded in swarm dynamics in a structurally meaningful way. The present study extends those previously published single-role variant results. It introduces a unified role-based framework for informed diversity control. The main benefit, we show, comes not from isolated operators, but from composing behavioral roles across the social and cognitive velocity components. The framework organizes diversity mechanisms into three families: repulsion from best solutions, attraction toward worst solutions, and repulsion from worst solutions. Beyond single-role variants, we introduce paired-role formulations and three hybrid architectures allowing different roles to act on different velocity components within the same swarm. Empirical evaluation on 32 benchmark functions across dimensionalities up to 1000 reveals that these multi-hybrid strategies consistently outperform standard PSO and remain highly competitive with state-of-the-art algorithms such as CMA-ES and L-SHADE in high-dimensional spaces.
Piotr Urbańczyk, Aleksandra Urbanczyk, Leszek Rutkowski et al.· Vietnam Journal of Computer...· 0 citations
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.
Yao-Pei Wang, Yufeng Wang, Ke Liu· Algorithms· 0 citations
Adaptive Hybrid PSO (AHPSO), which uses a sigmoid function on swarm diversity to automatically modulate gradient influence: near-zero during exploration, near-maximum during exploitation, with no manual phase-switching, is proposed.
Particle swarm optimization (PSO) is a widely used bio-inspired optimization method. However, global guidance can align particle trajectories, reduce population diversity, and lead to premature convergence. To address this issue, we propose GRACE-PSO, a particle swarm optimizer based on group rank assessment and cooperative evolution. The method introduces group-best learning as an intermediate layer between personal-best and global-best learning to improve the balance between exploration and exploitation. GRACE-PSO integrates three coupled mechanisms: (1) a group-learning update that provides the population with multiple group-specific search directions; (2) a rank-based group-utility assessment that evaluates relative search effectiveness through pairwise comparisons of personal-best fitness values; and (3) a utility-driven adaptive strategy that adjusts the strengths of group-best and global-best learning and selectively reinitializes a small number of underperforming particles in stagnant groups. Experiments on 29 CEC 2017 benchmark functions at 30 and 50 dimensions against seven representative PSO methods show that GRACE-PSO achieves average ranks of 1.2414 and 1.3793, respectively. Experiments on two practical flexible intelligent metasurface optimization problems further demonstrate its competitive performance and practical applicability.
Bio-inspired swarm metaheuristic algorithms constitute a widely used tool for solving complex optimization problems. However, the experimental characterization of their emergent behavior remains a methodological challenge. This study proposes an experimental framework for characterizing emergent behavior through swarm collective dynamics. The framework integrates complementary dynamic indicators and establishes relative diversity loss as a homogeneous criterion for defining equivalent comparison states across different search processes. The framework was evaluated using the Reptile Search Algorithm (RSA) and Draco Lizard Optimizer (DLO) as case studies, with Particle Swarm Optimization (PSO) serving as a reference algorithm. The results showed that swarm collective dynamics were associated with both the mathematical properties of the search landscape and the search mechanisms of each metaheuristic. Furthermore, relative diversity loss enabled the comparison of different metaheuristics within a common reference framework. In RSA, swarm reorganization occurred during the first iterations. DLO exhibited a more gradual evolution, whereas PSO showed an intermediate behavior between both dynamics. The proposed experimental framework provides a methodological basis for the experimental characterization of emergent behavior in swarm metaheuristics.
Sand Cat Swarm Optimization (SCSO) is a swarm intelligence algorithm characterized by a simple structure and a small number of control parameters. However, when solving complex optimization problems, SCSO suffers from several limitations, including an uneven initial population distribution, excessive dependence on the current best individual during the search process, insufficient local exploitation accuracy, and susceptibility to local optima. To address these limitations, a Collaborative Multi-Strategy Sand Cat Swarm Optimization algorithm (CMSCSO) is proposed. The good point set method is adopted to generate a uniformly distributed initial population. An adaptive random reuse strategy is designed to selectively inherit dimensional information from the best individual according to differences in individual fitness. A prey impatience coefficient is introduced to dynamically adjust the local search intensity according to the distance between the population and the current best solution. In addition, a refractive-mechanism-based opposition-based learning strategy for the worst individuals is incorporated to update low-quality individuals and improve the ability of the algorithm to escape from local optima. CMSCSO was evaluated using the 30-dimensional CEC2017 and 10-dimensional CEC2022 benchmark suites. Its performance was compared with that of SCSO and several recently developed metaheuristic algorithms. The experimental results show that CMSCSO achieved the best mean values on 24 of the 29 CEC2017 benchmark functions and on 10 of the 12 CEC2022 benchmark functions. In the Wilcoxon tests conducted on CEC2017 and CEC2022, CMSCSO achieved 220 and 90 statistically significant wins, respectively. It also ranked first in the Friedman tests for both benchmark suites. For engineering optimization problems, the results obtained from six types of engineering design problems demonstrate that CMSCSO can consistently obtain high-quality feasible solutions that satisfy the specified constraints. In two-dimensional and three-dimensional wireless sensor network coverage optimization problems, coverage rates of 96.30% and 89.54% were achieved. For photovoltaic model parameter identification, CMSCSO achieved the highest identification accuracy. The numerical and engineering test results demonstrate that CMSCSO provides high optimization accuracy, strong stability, and good adaptability to complex engineering problems. It can therefore serve as an effective solution method for optimization tasks in structural design, mechanical engineering, and other related fields.
