A collaboration-based particle swarm optimizer with history-guided estimation for optimization in dynamic environments

• Published in: Expert systems with applications 2019-04, Vol.120, p.1-13
• Main Authors: Cao, Leilei, Xu, Lihong, Goodman, Erik D.
• Format: Article
• Language: English
• Subjects: Agricultural management; Algorithms; Change detection; Collaboration; Collaborative mechanism; Dynamic optimization; Energy management systems; History-guided estimation; Hybrid electric vehicles; Hybrid systems; Intelligent systems; Job shop scheduling; Particle swarm optimization; Particle swarm optimizer; Performance enhancement
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2018.11.020

•A collaborative mechanism is proposed to improve particle swarm optimization.•A worst-replacement scheme is proposed to update particles’ positions.•The trajectory of the best particle during optimizing is stored to an external archive.•The stored solutions estimate a promising optimal region in a new environment.•The proposed algorithm shows a competitive power in dynamic optimization problems. Optimization problems widely exist in many expert and intelligent systems, e.g., greenhouse intelligent control systems in agriculture, energy management systems for hybrid electric vehicle, and job shop scheduling systems in manufacture. For the optimization problems in these systems, the objective functions may change over time. This kind of problem is usually called as dynamic optimization problems (DOPs) or optimizing in dynamic environments. The optimization algorithm plays an important role in designing an expert and intelligent system. In this paper, we present a novel particle swarm optimizer for optimization in dynamic environments. We introduce two schemes to improve performance of particle swarm optimization in dynamic environments. Firstly, the classical particle swarm optimization is enhanced by a collaborative mechanism, in which a target particle learns from another randomly selected particle and the global best one in the swarm. Instead of moving to the new position directly, a worst replacement operator is used to update the swarm, whereby the worst particle in the swarm moves to the better newly generated position. During optimizing, the best solution in each generation is stored. When an environmental change is detected, the historical solutions are retrieved to collaborate with some newly generated solutions to adapt to the new environment. The performance of the proposed algorithm is compared with several reported algorithms over the benchmark problems. Experimental results indicate that the proposed algorithm offers superior performance compared with the competitors.