AHPS2: An optimizer using adaptive heterogeneous particle swarms

• Multiple heterogeneous swarms with each consisting a group of homogeneous particles are modeled.
• Nature’s law, “survival of the fittest”, is simulated by proposing the adaptive competition strategy with two models.
• Heterogeneous learning based on two complementary techniques are theoretically analyzed and experimentally tested.
• Adaptive competition strategy with immigration model is more effective in practice.
• The proposed method shows a better or comparable performance than the comparison algorithms over the numerical experiments.

Particle swarm optimization (PSO) has suffered from premature convergence and lacked diversity for complex problems since its inception. An emerging advancement in PSO is multi-swarm PSO (MS-PSO) which is designed to increase the diversity of swarms. However, most MS-PSOs were developed for particular problems so their search capability on diverse landscapes is still less than satisfactory. Moreover, research on MS-PSO has so far treated the sub-swarms as cooperative groups with minimum competition (if not none). In addition, the size of each sub-swarm is set to be fixed which may encounter excessive computational cost. To address these issues, a novel optimizer using Adaptive Heterogeneous Particle SwarmS (AHPS2) is developed in this research. In AHPS2, multiple heterogeneous swarms, each consisting of a group of homogenous particles having similar learning strategy, are introduced. Two complementary search techniques, comprehensive learning and a subgradient method, are studied. To best take advantage of the heterogeneous learning strategies, an adaptive competition strategy is proposed so the size of each swarm can be dynamically adjusted based on its group performance. The analyses of the swarm heterogeneity and the competition models are presented to validate the effectiveness. Furthermore, comparisons between AHPS2 and state-of-the-art algorithms are grouped into three categories: 36 regular benchmark functions (30-dimensional), 20 large-scale benchmark functions (1000-dimensional) and 3 real-world problems. Experimental results show that AHPS2 displays a better or comparable performance compared to the other swarm-based or evolutionary algorithms in terms of solution accuracy and statistical tests.