A New Multi-swarm Particle Swarm Optimization for Robust Optimization Over Time

• Published in: Applications of Evolutionary Computation Vol. 10200; pp. 99 - 109
• Main Authors: Yazdani, Danial, Nguyen, Trung Thanh, Branke, Juergen, Wang, Jin
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Benchmark problems; Dynamic optimization; Multi-swarm algorithm; Particle swarm optimization; Robust optimization; Robust optimization over time; Tracking moving optima
• ISBN: 9783319557915; 3319557912
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-55792-2_7

Dynamic optimization problems (DOPs) are optimization problems that change over time, and most investigations in this area focus on tracking the moving optimum efficiently. However, continuously tracking a moving optimum is not practical in many real-world problems because changing solutions frequently is not possible or very costly. Recently, another practical way to tackle DOPs has been suggested: robust optimization over time (ROOT). In ROOT, the main goal is to find solutions that can remain acceptable over an extended period of time. In this paper, a new multi-swarm PSO algorithm is proposed in which different swarms track peaks and gather information about their behavior. This information is then used to make decisions about the next robust solution. The main goal of the proposed algorithm is to maximize the average number of environments during which the selected solutions’ quality remains acceptable. The experimental results show that our proposed algorithm can perform significantly better than existing work in this aspect.