Welfare State Optimization

In this paper, we propose a new evolutionary optimization algorithm called Welfare State Optimization (WSO) for solving optimization problems. In this algorithm, we emulate the behavior of welfare states to improve the lives of their citizens. The work is motivated by the fact that the welfare state optimally uses its resources (optimization) and restricts a group to lead the whole nation to a specific direction (local trap). So, the behavior of a welfare state is quite suitable for optimization algorithms. The proposed WSO algorithm is validated using ten standard benchmark functions and its performance is compared with five different variants of Particle Swarm Optimization (PSO) available in the literature. The results of our experiments are very promising and confirm the validity of the proposed approach. Hence, WSO algorithm can be considered as a strong alternative to solve optimization problems.