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 a high dimensional solution space of an optimization problem. Like some other evolutionary optimization techniques, premature convergence is also happened in WWO. 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 local optima more easily. The Modified Water Wave Optimization (MWWO) is evaluated on some benchmark function and the results are reported.