Multi Objective Particle Swarm Optimization based Cooperative Agents with Automated Negotiation
This addresses stagnation issues in MOPSO for optimization problems, but it is incremental as it builds on existing methods.
The paper tackles stagnation in multi-objective particle swarm optimization (MOPSO) by hybridizing it with cooperative agents and automated negotiation, resulting in improved trade-off between exploitation and exploration compared to other algorithms.
This paper investigates a new hybridization of multi-objective particle swarm optimization (MOPSO) and cooperative agents (MOPSO-CA) to handle the problem of stagnation encounters in MOPSO, which leads solutions to trap in local optima. The proposed approach involves a new distribution strategy based on the idea of having a set of a sub-population, each of which is processed by one agent. The number of the sub-population and agents are adjusted dynamically through the Pareto ranking. This method allocates a dynamic number of sub-population as required to improve diversity in the search space. Additionally, agents are used for better management for the exploitation within a sub-population, and for exploration among sub-populations. Furthermore, we investigate the automated negotiation within agents in order to share the best knowledge. To validate our approach, several benchmarks are performed. The results show that the introduced variant ensures the trade-off between the exploitation and exploration with respect to the comparative algorithms