Coevolutionary Particle Swarm Optimization With Bottleneck Objective Learning Strategy for Many-Objective Optimization

Abstract

The application of multiobjective evolutionary algorithms to many-objective optimization problems often faces challenges in terms of diversity and convergence. On the one hand, with a limited population size, it is difficult for an algorithm to cover different parts of the whole Pareto front (PF) in a large objective space. The algorithm tends to concentrate only on limited areas. On the other hand, as the number of objectives increases, solutions easily have poor values on some objectives, which can be regarded as poor bottleneck objectives that restrict solutions' convergence to the PF. Thus, we propose a coevolutionary particle swarm optimization with a bottleneck objective learning (BOL) strategy for many-objective optimization. In the proposed algorithm, multiple swarms coevolve in distributed fashion to maintain diversity for approximating different parts of the whole PF, and a novel BOL strategy is developed to improve convergence on all objectives. In addition, we develop a solution reproduction procedure with both an elitist learning strategy (ELS) and a juncture learning strategy (JLS) to improve the quality of archived solutions. The ELS helps the algorithm to jump out of local PFs, and the JLS helps to reach out to the missing areas of the PF that are easily missed by the swarms. The performance of the proposed algorithm is evaluated using two widely used test suites with different numbers of objectives. Experimental results show that the proposed algorithm compares favorably with six other state-of-the-art algorithms on many-objective optimization.

A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization Problems

“…However, this method is very sensitive to the used parameters and requires a high-computational cost. In CPSO [29], a coevolutionary PSO with bottleneck objective learning strategy was designed for solving MaOPs. Multiple swarms coevolved in a distributed fashion to maintain diversity for approximating the entire PFs, while a novel bottleneck objective learning strategy was used to accelerate convergence for all objectives.…”

“…e first challenge is to provide the sufficient selection pressure to approach the true PFs of MaOPs, i.e., the challenges in maintaining convergence. With the increase of objectives in MaOPs, it becomes very difficult for MOPSOs to pay the same attention to optimize all the objectives, which may lead to an imbalanced evolution such that solutions are very good at solving some objectives but perform poorly on the others [29]. Moreover, most solutions of MaOPs are often nondominated with each other at each generation.…”

Cooperative Coevolutionary Bare-Bones Particle Swarm Optimization With Function Independent Decomposition for Large-Scale Supply Chain Network Design With Uncertainties

“…Nevertheless, SCND is still a developing research topic and many new features may appear in the new application scenarios. Therefore, future work will include solving the LUSCND problems with more factors, such as environmental dimensions [2], social dimensions [12], and routing decisions [41], which may be solved by the multiobjective [42]- [44], many-objective [45], and multimodal [46]- [48] algorithms.…”

Solving the Energy Efficient Coverage Problem in Wireless Sensor Networks: A Distributed Genetic Algorithm Approach with Hierarchical Fitness Evaluation

“…With the development of evolutionary computations (ECs) like GA [25,26], ant colony optimization (ACO) [27,28], particle swarm optimization (PSO) [29,30], and differential evolution (DE) [31,32], many researchers have applied ECs into solving the EEC problems in WSN, such as PSO-based [33] and ACO-based [34] approaches. Specifically, in [33], Zhan et al extended the binary PSO (BPSO) to solve the EEC problem by finding a minimal set of nodes again and again to maximize the number of disjoint sets.…”