Context. Niching genetic algorithms are one of the most popular approaches to solve multimodal optimization problems. When classifying niching genetic algorithms it is possible to select algorithms explicitly analyzing topography of fitness function landscape; multinational genetic algorithm is one of the earliest examples of these algorithms.
Objective. Development and analysis of the multinational genetic algorithm and its modifications to find all maxima of a multimodal function.
Method. Experimental analysis of algorithms is carried out. Numerous runs of algorithms on well-known test problems are conducted and performance criteria are computed, namely, the percentage of convergence, real (global, local) and fake peak ratios; note that peak rations are computed only in case of algorithm convergence.
Results. Software implementation of a multinational genetic algorithm has been developed and experimental tuning of its parameters has been carried out. Two modifications of hill-valley function used for determining the relative position of individuals have been proposed. Experimental analysis of the multinational genetic algorithm with classic hill-valley function and with its modifications has been carried out.
Conclusions. The scientific novelty of the study is that hill-valley function modifications producing less number of wrong identifications of basins of attraction in comparison with classic hill-valley function are proposed. Using these modifications yields to performance improvements of the multinational genetic algorithm for a number of test functions; for other test functions improvement of the quality criteria is accompanied by the decrease of the convergence percentage. In general, the convergence percentage and the quality criterion values demonstrated by the algorithm studied are insufficient for practical use in comparison with other known algorithms. At the same time using modified hill-valley functions as a post-processing step for other niching algorithms seems to be a promising improvement of performance of these algorithms.
