Slime Mould Algorithm (SMA) is a new meta-heuristics algorithm that inspired by the behaviors of slime mould from nature. Due to its effective performance, SMA has shown competitive performance among other algorithms and been used in many mathematical optimization real-world problems. However, tends sink into local optimality lacks diversity population. Therefore, cope with drawbacks classical ...

Abstract The differential evolution (DE) algorithm is an efficient random search based on swarm intelligence for solving optimization problems. It has the advantages of easy implementation, fast convergence, strong ability and good robustness. However, performance DE very sensitive to design different operators setting control parameters. To solve these key problems, this paper proposes improve...

In this article, we introduce an improved optimization based technique for the synthesis of circular antenna array. The main objective is to achieve minimum side lobe levels, maximum directivity and null control for the non-uniform, planar circular antenna array. The design procedure utilizes an improved variant of a prominent and efficient metaheuristic algorithm of current interest, namely th...

This paper presents a modified Differential Evolution (DE) algorithm called OCMODE for solving multi-objective optimization problems. First, the initialization phase is improved by using the opposition based learning. Further, a time varying scale factor F employing chaotic sequence is used which helps to get a well distributed Pareto front by the help of non dominated and crowding distance sor...

Differential evolution is an efficient and powerful population-based stochastic technique capable of handling non-differentiable, non-linear and multi-modal objective functions. In order to improve its performance, this paper introduces a best-trigonometric mutation strategy and applies a crossover rate update strategy to the proposed algorithm. The performance of the proposed algorithm is inve...