A multiagent evolutionary framework based on trust for multiobjective optimization

Experimental studies on 35 benchmark MOPs show that the proposed novel multiagent evolutionary framework significantly improves the performance of the state-of-the-art EAs.

Abstract

In an Evolutionary Algorithm (EA) for optimization problems, candidate solutions to the problems are individuals in a population. They produce offsprings by taking evolutionary operators with user-specific control parameters. The challenge is then how to effectively select evolutionary operators and adjust control parameters from generation to generation and on different problems. We propose a novel multiagent evolutionary framework based on trust where each solution is represented as an intelligent agent, and evolutionary operators and control parameters are represented as services. Agents select services in each generation based on trust that measures the competency or suitability of the services for solving particular problems. Multiobjective Optimization Problems (MOPs) are used to showcase the value of our framework. Experimental studies on 35 benchmark MOPs show that our framework significantly improves the performance of the state-of-the-art EAs.