A New Multi-swarm Particle Swarm Optimization for Robust Optimization Over Time
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.
KeywordsRobust optimization over time Robust optimization Dynamic optimization Benchmark problems Tracking moving optima Particle swarm optimization Multi-swarm algorithm
This work is supported by a Dean Scholarship by the Faculty of Engineering and Technology, Liverpool John Moores University, and is partially supported by a T-TRIG project by the UK Department for Transport, a Newton Institutional Links project by the UK BEIS via the British Council, a Newton Research Collaboration Programme (3) by the UK BEIS via the Royal Academy of Engineering, and a Seed-corn project funded by the Chartered Institute of Logistics and Transport.
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