A New Variant Of Firefly Algorithm For Global Optimization

Abstract

Firefly algorithm (FFA) is the most recent Swarm Intelligence (SI) Algorithm which is based on flashing of light by fireflies. This algorithm considers each firefly as a possible solution and brightness of each firefly depends on their performance over optimizing problem. The swarm of firefly moves towards the goal by following the brighter firefly and if there is no brighter firefly they will move randomly. The basic FFA algorithm follows a specific update strategy of attraction as well as movement of swarm of fireflies which does not produce good quality results and converge at low rate. Keeping this flaw of FFA a new algorithm is proposed that directs the randomly moving firefly to the brightest firefly in the current iteration. To maintain variety and avoid early convergence directions are given at certain refresh rate. The performance of New variant FFA (NvFFA) is validated against various SI algorithms over standard test cases which can be complex, multimodal and scalable optimization problems whose dimensions can be 10, 30 and 50. The proposed algorithm will prove that quality of solution and convergence rate is improved.