A Real-Coded Niching Memetic Algorithm for Continuous Multimodal Function Optimization

In this work we extend the sequential niching technique of Beasley et at. for multiple optimal determination, incorporating a local search to improve accuracy. In the proposed method a sequence of GA runs make use of a derating function and of niching and clearing techniques to promote the occupation of different niches in the function to be optimized. The algorithm searches the solution space eliminating from the fitness landscape previously located peaks forcing the individuals to converge into unoccupied niches. Unlike other algorithms the efficiency of this sequential niching memetic algorithm (SNMA) is not highly sensitive to the niche radius. Performance measurements with standard test functions used by other researchers, show that the SNMA proposed outperforms other algorithms in accurately locating all optima, both global and local, in the search space.