Leader Prediction for Multiobjective Particle Swarm Optimization

Abstract

In the design of multiobjective particle swarm optimization (MOPSO) algorithms, swarm leaders, i.e., the personal best (pbest) and global best (gbest), are expected to guide the particles toward Pareto-optimal solutions. However, most existing MOPSO algorithms focus on selecting such leaders from the archive of candidate solutions to approximate the Pareto front (PF) that may not yield good approximations of the Pareto set (PS). To address this challenge, this work proposes to predict both pbest and gbest for each particle by explicitly approximating the manifold structure of the PS, following the regularity property of multiobjective optimization problems. Thus, we design a leader prediction-based MOPSO (PPSO) algorithm. In our algorithm, a self-organizing mapping (SOM) method is adopted at each iteration to capture the manifold structure from the current swarm to predict leaders. Specifically, pbest is pinpointed by mapping the particle onto the neuron of SOM, while gbest is estimated by randomly selecting from the neighborhood neurons. In this way, the particles of a swarm in PPSO are guided by the predicted pbest and gbest to approximate the Pareto-optimal solutions. The developed PPSO is empirically verified with several representative algorithms, on several benchmark test instances and real-world problems. Experimental results have demonstrated the advantages of leader prediction for MOPSO over other approaches.