Covariance Matrix Adaptation for Multi-objective Optimization
Evolutionary Computation
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
Agent-Based Evolutionary Search
Agent-Based Evolutionary Search
Effective Web Service Selection via Communities Formed by Super-Agents
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Evolutionary multi-objective optimization: a historical view of the field
IEEE Computational Intelligence Magazine
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters
IEEE Transactions on Evolutionary Computation
A multiagent genetic algorithm for global numerical optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An evolutionary model for constructing robust trust networks
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Towards the design of robust trust and reputation systems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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.