A Hybrid Leader Selection Strategy for Many-Objective Particle Swarm Optimization

Abstract

Many existing Multi-objective Particle Swarm Optimizers (MOPSOs) may encounter difficulties for a set of good approximated solutions when solving problems with more than three objectives. One possible reason is that the diluted selection pressure causes MOPSOs to fail to generate a set of good approximated Pareto solutions. In this paper, a new approach called the Hybrid Global Leader Selection Strategy (HGLSS) is proposed to deal with many-objective problems more effectively. HGLSS provides two global leader selection mechanisms: one for exploration and one for exploitation. Each particle (solution) can choose one of these two leader selection schemes to identify its global best leader. An external archive is adopted for maintaining the diversity of the found solutions and it contains the final solution reported at the end of the run. The update of the external archive is based on both Pareto dominance and density estimation. The performance of the proposed approach is compared with respect to nine state-of-the-art multi-objective metaheuristics in solving several benchmark problems. Our results indicate that the proposed algorithm generally outperforms the others in terms of Modified Inverted Generational Distance (IGD +) indicator.

Entropy‐Driven Global Best Selection in Particle Swarm Optimization for Many‐Objective Software Package Restructuring

“…Li et al [45] used grid-based ranking approach to improve the discriminability of the particles in many-objective PSO to choose personal best and global best. Leung et al [19] proposed a Hybrid Global Leader Selection Scheme (HGLSS) using the concepts of space expanding strategy (SES) and Euclidean distance strategy (EDS). Yang et al [20] designed a vector angle and decomposition-based method to select the personal best and global best for the many-objective PSO approach.…”

“…To solve the real-world MaOPs (e.g., many-objective software remodularization), several researchers and practitioners have customized the existing metaheuristic optimization algorithms (e.g., many-objective software remodularization using NSGA-III by [8]). Recently, particle swarm optimization (PSO) based on many-objective optimization algorithms has been widely explored to address the different science and engineering MaOPs (e.g., [16][17][18][19][20]). In the PSObased many-objective optimization, the selection of leaders (i.e., personal best or global best) has the major influence on the generation of well-distributed and converged approximation of the Pareto front.…”

A Statistical Comparison of Metaheuristics for Unrelated Parallel Machine Scheduling Problems with Setup Times

“…The authors verified that the proposed model obtained better results when compared to the PSO. In another paper [11], a new approach was applied in order to improve the performance of multi-objective particle swarm optimizers (MOPSOs). The strategy used is called hybrid global leader selection (HGLSS), where Pareto dominance and density estimation are analyzed to verify the effectiveness of the proposed model.…”

A Hybrid Multi-Objective Teaching Learning-Based Optimization Using Reference Points and R2 Indicator

“…Finally, the algorithm's performance is evaluated by various functions from three well-known benchmark cost functions (ZDT, DTLZ and CEC09). Recently, hybrid algorithms have been proposed [18,27], but most are restricted to popular SI and EA algorithms such as PSO and GA. Here we benchmark the proposed R2-HMTLBO algorithm and demonstrate that achieves SOTA performance when compared to NSGA-III, MOEA/D, MOMBI-II and MOEA/IGD-NS in nineteen test problems.…”