An agent-based memetic algorithm (AMA) for nonlinear optimization with equality constraints
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Constrained optimization based on modified differential evolution algorithm
Information Sciences: an International Journal
Estimating meme fitness in adaptive memetic algorithms for combinatorial problems
Evolutionary Computation
A distributed agent-based approach for simulation-based optimization
Advanced Engineering Informatics
Comprehensive Survey of the Hybrid Evolutionary Algorithms
International Journal of Applied Evolutionary Computation
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Memetic algorithms (MA) have recently been applied successfully to solve decision and optimization problems. However, selecting a suitable local search technique remains a critical issue of MA, as this significantly affects the performance of the algorithms. This paper presents a new agent based memetic algorithm (AMA) for solving constrained real-valued optimization problems, where the agents have the ability to independently select a suitable local search technique (LST) from our designed set. Each agent represents a candidate solution of the optimization problem and tries to improve its solution through co-operation with other agents. Evolutionary operators consist of only crossover and one of the self-adaptively selected LSTs. The performance of the proposed algorithm is tested on five new benchmark problems along with 13 existing well-known problems, and the experimental results show convincing performance.