Multiagent Swarm Optimization With Adaptive Internal and External Learning for Complex Consensus-Based Distributed Optimization

Distributed optimization has attracted lots of attention in recent years. Thanks to the intrinsic parallelism and great search capacity, evolutionary computation (EC) has the potential for black-box and non-convex distributed optimization. However, due to the decentralization of local objective functions, it is challenging to optimize the global objective function with efficient communication and guaranteed system consensus. To tackle this challenge, we propose a Multi-Agent Swarm Optimization method with adaptive Internal and External learning (MASOIE). In MASOIE, each agent evolves a swarm of particles by internal learning and external learning. Internal learning enables agents to optimize their local objectives, while external learning enables agents to cooperate to achieve a consensus toward the global objective. To improve the consensus ability, we design a special velocity setting of external learning for particle evolution. We provide the theoretical analysis of the system consensus of deterministic MASOIE. To improve communication efficiency, we design an adaptive communication mechanism to adjust the communication interval, enabling agents to explore at the early stage and reach system consensus at the later stage. Empirical studies show that the proposed algorithm achieves stable consensus performance, competitive solution quality and lower communication cost on benchmark functions compared with existing black-box distributed algorithms.