Differential evolution (DE) algorithms have been extensively and frequently applied to solve optimizationproblems. Theoretical analyses of their properties are important to understand the underlying mechanismsand to develop more efficient algorithms. In this paper, firstly, we introduce an absorbing Markovsequence to model a DE algorithm. Secondly, we propose and prove two theorems that provide...

In this paper, we propose a differential evolution (DE) algorithm which aims to optimize a system whose parameters form a multidimensional array. Determination of the optimal demixing matrix in a blind signal separation problem can be considered as an example of this type of system. Since the DE algorithm performs only on column vectors, it can be used after the multidimensional array is transf...

Differential Evolution (DE) algorithm is a new heuristic approach mainly having three advantages; finding the true global minimum regardless of the initial parameter values, fast convergence, and using few control parameters. DE algorithm is a population based algorithm like genetic algorithms using similar operators; crossover, mutation and selection. In this work, we have compared the perform...

Differential Evolution (DE) is a method of optimization used in symmetrical problems and also that are not even continuous, noisy change over time. DE optimizes problem with population candidate solutions creates new per generation combination existing rules according to discriminatory rules. The present work proposes two variations for this method. first significantly improves the termination ...

Abstract— Improving the transient performance of the MRAC has been a point of research for a long time. The main objective of the paper is to design an MRAC with improved transient and steady state performance. This paper proposes a Fuzzy modified MRAC (FMRAC) to control a coupled tank level process. The FMRAC uses a proportional control based Mamdani-type Fuzzy inference system (MFIS) to impro...

In Dynamic Economic Load Dispatch (DELD), optimization and evolution computation become a major part with the strategy for solving the issues. From various algorithms Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms are used to encode in a vector form and in sharing information and both approaches are based on the master-apprentice mechanism for the Dual Evolution St...

Differential Evolution (DE) proved to be one of the most successful evolutionary algorithms for global optimization purposes in continuous problems. The core operator in DE is mutation which can provide the algorithm with both exploration and exploitation. In this article, a new notation for DE is proposed which has a formula that can be utilized for generating and extracting novel mutations an...

In this paper, based on the fusion of the clonal selection algorithm (CSA) and differential evolution (DE) method, we propose a novel optimization scheme: CSA–DE. The DE is employed here to improve the affinities of the clones of the antibodies (Abs) in the CSA. Several nonlinear functions are used to verify and demonstrate the effectiveness of our hybrid optimization approach. It is further ap...