A Wind Driven Approach Using Lévy Flights for Global Continuous Optimization

Recently, the metaheuristics have drawn a great attention to researchers. The drawbacks of existing derivative-based numerical methods have forced the researchers to rely on metaheuristics founded on simulations to solve scientific computation and engineering optimization problems. A common feature shared by the metaheuristics is that they combine rules and randomness to imitate some natural phenomena. Wind driven optimization (WDO) belongs to optimization metaheuristic algorithm. It is a stochastic nature-inspired global optimization method based on atmospheric motion. In this paper, we focus our study on an enhanced WDO using Lévy flights (WDOLE) applied to global optimization in the continuous domain. To evaluate the performance of the proposed WDOLE, well-known unconstrained benchmark functions in the literature are optimized using the proposed WDOLE, and provides comparisons with the standard WDO.

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