In recent years, the Particle Swarm Optimization has rapidly gained increasing popularity and many variants and hybrid approaches have been proposed to improve it. In this paper, motivated by the behavior and the spatial characteristics of the social and cognitive experience of eachparticle in the swarm,wedevelop ahybrid framework that combines theParticle Swarm Optimization and the Differentia...

Differential Evolution (DE) is a powerful metaheuristic method used to numerically optimize functions or multidimensional problems not solved by traditional methods of global optimization. In turn, if boundary conditions are added, constraint handling techniques should be need. To improve DE’s performance, algorithms of local search are a good alternative. Hybridization of Differential Evolutio...

Differential Evolution (DE) is a simple but powerful evolutionary optimization algorithm with many successful applications. In this paper we propose Differential Evolution for Multiobjective Optimization (DEMO) – a new approach to multiobjective optimization based on DE. DEMO combines the advantages of DE with the mechanisms of Paretobased ranking and crowding distance sorting, used by state-of...

CODEQ is a new, population-based meta-heuristic algorithm that is a hybrid of concepts from chaotic search, opposition-based learning, differential evolution and quantum mechanics. CODEQ has successfully been used to solve different types of problems (e.g. constrained, integer-programming, engineering) with excellent results. In this paper, CODEQ is used to train feed-forward neural networks. T...

Evolutionary Algorithms (EAs) are stochastic and robust meta-heuristics of evolutionary computation field useful to solve optimization problems in image processing applications. Recently, as special mechanism to avoid being trapped in local minimum, the ergodicity property of chaotic sequences has been used in various designs of EAs. Three differential evolution approaches based on chaotic sequ...

The general purpose optimization method known as Differential Evolution (DE) has a number of parameters that determine its behaviour and efficacy in optimizing a given problem. This paper gives a list of good choices of parameters for various optimization scenarios which should help the practitioner achieve better results with little effort.

Created in 1994, Differential Evolution (DE) is relatively new but already has been proven to perform well for optimisation in static environments. But with many realworld optimisation problems having non-static environments, how well can DE perform in these situations? What changes can be done to allow DE to perform better in dynamic environments?

─ The application of optimization techniques for shape reconstruction of a perfectly conducting two-dimensional cylinder buried in a slab medium is reported in this paper, for which comparative study of four population-based optimization algorithms are conducted. The method of finite difference time domain (FDTD) is employed for the analysis of the forward scattering part, while the inverse sca...

Many methods for solving optimal control problems, whether direct or indirect, rely upon gradient information and therefore may converge to a local optimum. Global optimisation methods like Evolutionary algorithms, overcome this problem. In this work it is investigated how well novel and easy to understand Evolutionary algorithms, referred to as Differential Evolution (DE) algorithms, and claim...

Stochastic (or probabilistic) programming (SP) is an optimization technique in which the constraints and/or the objective function of an optimization problem contain random variables. The mathematical models of these problems may follow any particular probability distribution for model coefficients. The objective here is to determine the proper values for model parameters influenced by random e...