A Modified Fitness Based Differential Evolution Using Population Based Levy Flight Strategy (MFBDE_PLS)

Abstract

Research workers solve the simple problems of optimization by using various mathematical techniques. But to solve complex problems of optimization a stochastic and population based algorithm named Differential Evolution (DE) is used. DE is fast, simple and straightforward algorithm to optimize the problems which are complex. Like other evolutionary algorithms DE also has some drawbacks. In FBDE author tried to overcome these drawbacks and tested on some benchmark functions and real world problems. FBDE is also found good as compared to DE and its recent variants. The proposed algorithm is a modification of FBDE using my previously published algorithm PGLFDE. In PGLFDE to make the balance between DE's exploration and exploitation capabilities levy flight search is modified using different mutation strategies for different population. In proposed algorithm we are hybridizing the PGLFDE into the FBDE to improve the performance and efficiency of FBDE and its name is called MFBDE_PLS. The proposed algorithm is to be tested on various standard benchmarks and real world problems and to personify with some modified DE algorithms.