The use of evolutionary algorithms (EAs) to solve problems with multiple objectives (known as Vector Optimization Problems (VOPs)) has attracted much attention recently. Being population based approaches, EAs offer a means to find a group of pareto–optimal solutions in a single run. Differential Evolution (DE) is an EA that was developed to handle optimization problems over continuous domains. ...

This paper presents a hybrid differential evolution, particle swarm optimization and fuzzy c-means clustering algorithm called DEPSO-FCM for image segmentation. By the use of the differential evolution (DE) algorithm and particle swarm optimization to solve the FCM image segmentation influenced by the initial cluster centers and easily into a local optimum. Empirical results show that the propo...

In this article, we propose the Mapping-based Pseudo Binary Differential Evolution (MPBDE) algorithm for solving discrete optimization problems. Based on the framework of the standard DE, a new boundary constraint-handling operator ensures that the results of the mutation operator meet the boundary conditions. Then the real values of the genotypes mapped into the discrete ones in the phenotypes...

The slow convergence and local minima problems associated with neural networks (NN) used for non-linear system identification have been resolved by evolutionary techniques such as differential evolution (DE) combined with Levenberg Marquardt (LM) algorithm. In this work the authors attempted further to employ an opposition based learning in DE, known as opposition based differential evolution (...

Differential Evolution (DE) is an evolutionary optimization technique that is exceptionally simple, fast, and robust at numerical optimization. However, the convergence rate of DE in optimizing a computationally expensive objective function still does not meet our requirements, and an attempt to speed up DE is considered necessary. This paper introduces a Modified Differential Evolution (MDE) t...

In order to improve the weak situation of the global search ability, the stability and time consuming of optimization of differential evolution(DE) algorithm in solving high dimensional optimization problem, an improved differential evolution algorithm with multipopulation and multi-strategy(MPMSIDE) is proposed to solve high dimensional optimization problem. Firstly, the different DE mutation ...

In this paper, design of an aperture-coupled microstrip antenna (ACMSA) using differential evolution algorithm (DE) is described. The classical transmission line model for microstrip antenna is used to determine fitness function for DE and computed results are compared with the results obtained using particle swarm optimization (PSO) and binary coded genetic

This paper proposes a new differential evolution (DE) algorithm for unconstrained continuous optimisation problems, termed μJADE, that uses a small or ‘micro’ (μ) population. The main contribution of the proposed DE is a new mutation operator, ‘current-by-rand-to-pbest.’ With a population size less than 10, μJADE is able to solve some classical multimodal benchmark problems of 30 and 100 dimens...

— In this paper, the performances of the quasi-Newton BFGS algorithm, the NEWUOA derivative free optimizer, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), the Differential Evolution (DE) algorithm and Particle Swarm Optimizers (PSO) are compared experimentally on benchmark functions reflecting important challenges encountered in real-world optimization problems. Dependence of the...

— In this paper, the performances of the quasi-Newton BFGS algorithm, the NEWUOA derivative free optimizer, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), the Differential Evolution (DE) algorithm and Particle Swarm Optimizers (PSO) are compared experimentally on benchmark functions reflecting important challenges encountered in real-world optimization problems. Dependence of the...