Learning algorithm for multimodal optimization

We present a new evolutionary algorithm—“learning algorithm” for multimodal optimization. The scheme for reproducing a new generation is very simple. Control parameters, of the length of the list of historical best solutions and the “learning probability” of the current solutions being moved towards the current best solutions and towards the historical ones, are used to assign different search intensities to different parts of the feasible area and to direct the updating of the current solutions. Results of numerical tests on minimization of the 2D Schaffer function, the 2D Shubert function and the 10D Ackley function show that this algorithm is effective and efficient in finding multiple global solutions of multimodal optimization problems.