Rooster Colony Algorithm: A two-step Multi-Swarm Optimization Approach

Abstract

Particle Swarm Optimization is a metaheuristic optimization algorithm inspired by the collective behavior of animal swarms where a set of candidate solutions, called particles, are randomly initialized in the search space, and their movements are iteratively updated based on their individual best solutions and the global best solution found by the swarm. This paper proposes a Multi-Swarm rooster colony algorithm (RCA) that considers a set of roosters, each owning a group of hens to compose a team. Each team (rooster and its hens) competes for the resource (food) with the other teams. From the combinatorial optimization point of view, each team analyzes part of the search space by an independent PSO algorithm with the same objective function. The RCA algorithm concurrently executes all PSO algorithms with different inertial weights for exploring different regions and the best solution (Gbest) of each team will compose the initial population for a new further centralized PSO algorithm that will exploit the previous solutions to search for the optimal one. Thus, the proposed RCA is composed of two steps, based on exploration and exploitation strategies to find an optimized solution in the search space. The results show that the proposed algorithm is competitive in solving well-known optimization functions. The objective is to apply this technique to solving real-life scheduling problems.