The vast use of Linear Prediction Coefficients (LPC) in speech processing systems has intensified the importance of their accurate computation. This paper is concerned with computing LPC coefficients using evolutionary algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Dif-ferential Evolution (DE) and Particle Swarm Optimization with Differentially perturbed Velocity (PSO-DV...

Many real life optimization problems are multimodal. Evolutionary Algorithms (EA) have successfully been applied to solve these problems, but have the disadvantage that converge to only one optimum, even though there are many optima. We proposed a hybrid algorithm combining differential evolution (DE) with tabu search (TS) to solve these problems, allowing to find multiple solutions. The propos...

Differential evolution (DE) is a versatile and efficient evolutionary algorithm for global numerical optimization, which has been widely used in different application fields. However, different strategies have been proposed for the generation of new solutions, and the selection of which of them should be applied is critical for the DE performance, besides being problem-dependent. In this paper,...

We propose a 2-population cooperative coevolutionary optimization method that can efficiently solve multi-objective optimization problems as it successfully combines positive traits from classic multi-objective evolutionary algorithms and from newer optimization approaches that explore the concept of differential evolution. A key part of the algorithm lies in the proposed dual fitness sharing m...

In the fashion industry, order scheduling focuses on the assignment of production orders to appropriate production lines. In reality, before a new order can be put into production, a series of activities known as pre-production events need to be completed. In addition, in real production process, owing to various uncertainties, the daily production quantity of each order is not always as expect...

Heuristic search for the global minimum is studied. This paper is focused on the adaptation of control parameters in differential evolution (DE) and in controlled random search (CRS). The competition of different control parameter settings is used in order to ensure the self-adaptation of parameter values within the search process. In the generalized CRS the self-adaptation is ensured by severa...

Optimal control of water resource systems plays an important role in sustainable development of a nation. In general, most of the water management problems are multi-objective in nature and often involve different types of complexities in their model formulation. Obtaining optimal solutions to such problems is always a challenging task. In multi-objective environment, in order to perceive the e...

This paper presents insight into an adaptation and self-adaptation mechanism within differential evolution, covering not only how but moreover – when this mechanism generates new values for control parameters, focusing on the iteration-temporal randomness of the self-adaptive control parameters. In particular, this randomness is controlled by a randomness level parameter, which influences the c...

Differential Evolution (DE) is one of the most successful and powerful evolutionary algorithms for global optimization problem. The most important operator in this algorithm is mutation operator which parents are selected randomly to participate in it. Recently, numerous papers are tried to make this operator more intelligent by selection of parents for mutation intelligently. The intelligent s...