A Novel Adaptive Stochastic Search Algorithm Based on Group Founding and Joining Behaviors

An adaptive stochastic search algorithm with hybrid strategy(HASS)is presented to improve the low search efficiency and the incompetitive optimization of the free search algorithm and the adaptive stochastic search algorithm(ASS).The algorithm is based on group founding and joining behaviors that exist widely in nature.The strategy to adaptively update the search radius of each individual is used to improve the search efficiency,and a hybrid search strategy is designed to guide different particles to respectively conduct global or local searches.A new mutation operator is introduced to the evolutionary state estimation of the involved particles to improve the population diversity and to avoid premature convergence effectively.The main differences between HASS and ASS are in selection mechanism of solutions and the search strategy.The experimental results on twelve classic benchmark functions show that the HASS algorithm has competitive performance to other four existing algorithms in terms of accuracy,robustness and convergence speed,especially for high-dimensional multimodal problems.