Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Non-genetic transmission of memes by diffusion
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems
Scalability of generalized adaptive differential evolution for large-scale continuous optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
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This paper presents a coevolutionary memetic particle swarm optimizer (CMPSO) for the global optimization of numerical functions. CMPSO simplifies the update rules of the global evolution and utilizes five different effective local search strategies for individual improvement. The combination of the local search strategy and its corresponding computational budget is defined as coevolutionary meme (CM). CMPSO co-evolves both CMs and a single particle position recording the historical best solution that is optimized by the CMs in each generation. The experimental results on 7 unimodal and 22 multimodal benchmark functions demonstrate that CMPSO obtains better performance than other representative state-of-the-art PSO variances. Particularly, CMPSO is shown to have higher convergence speed.