An Extension of Particle Swarm Optimization to Identify Multiple Peaks using Re-diversification in Static and Dynamic Environments

Abstract

We propose an extension of the particle swarm optimization (PSO) algorithm for each particle to store multiple global optima internally for identifying multiple (top-k) peaks in static and dynamic environments. We then applied this technique to search and rescue problems of rescuing potential survivors urgently in life-threatening disaster scenarios. With the rapid development of robotics andcomputer technology, aerial drones can be programmed to implement search algorithms that locate potential survivors and relay their positions to rescue teams. We model an environment of a disaster area with potential survivors using randomizedbivariate normal distributions. We extended the Clerk-Kennedy PSO algorithm as top-k PSO by considering individual drones as particles, where each particle remembers a set of global optima to identify the top-k peaks. By comparing several otheralgorithms, including the canonical PSO, Clerk-Kennedy PSO, and NichePSO, we evaluated our proposed algorithm in static and dynamic environments. The experimental results show that the proposed algorithm was able to identify the top-kpeaks (optima) with a higher success rate than the baseline methods, although the rate gradually decreased with increasing movement speed of the peaks in dynamic environments.