MWWO: Modified Water Wave Optimization

Water wave optimization (WWO) is a swarm intelligence optimization algorithm that shares many similarities with evolutionary computation techniques. However, the WWO uses three wave-inspired operators including propagation, refraction, and breaking for effectively exploring a high dimensional solution space of an optimization problem. Like some other evolutionary optimization techniques, premature convergence in the WWO is also. In this paper, we propose a new kind of exploration parameter to increase the exploration ability of algorithm. As the results show it helps the agent to escape the sub-optima more easily. The Modified Water Wave Optimization (MWWO) is evaluated on some benchmark function and the results are reported.