In this context, a novel structure has been proposed for simple Differential Evolutionary (DE) algorithm to solve optimal recloser placement. For this, an operator is added to DE algorithm to adapt concept of the problem. Other contribution of this work is formulating a novel objective function. The proposed objective function has been formulated to improve four reliability indices which consis...

In the present article we propose two novel variants of Differential Evolution (DE) namely Centroid Differential Evolution (CDE) and Differential Evolution with local Search (DELS) for solving unconstrained, single objective, optimization problems. Both the algorithms make use of geometric centroid to enhance the performance of basic DE in terms of average fitness function value as well as in t...

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). Over the last few decades, a number of Differential Evolution (DE) algorithms have been proposed with excellent performance on mathematical benchmarks. However, li...

Differential Evolution (DE) is a novel evolutionary approach capable of handling nondifferentiable, non-linear and multi-modal objective functions. DE has been consistently ranked as one of the best search algorithm for solving global optimization problems in several case studies. This paper presents an Improved Constraint Differential Evolution (ICDE) algorithm for solving constrained optimiza...

-Past literature has attempted to solve the problem of network reconfiguration with Distributed Generators (DGs) without taking into consideration the intermittent renewable at a close proximity. Distribution Network Reconfiguration (ADNR) must account uncertain behavior of loads and wind when the commercial wind based DG, Doubly Fed Induction Generators (DFIG) supports a significant part of ne...

Differential evolution (DE), as a class of biologically inspired and meta-heuristic techniques, has attained increasing popularity in solving many real world optimization problems. However, DE is not always successful. It can easily get stuck in a local optimum or an undesired stagnation condition. This paper proposes a new DE algorithm Differential Evolution with Alopex-Based Local Search (DEA...

Differential Evolution(DE) has emerged as a powerful and efficient evolutionary algorithm for solving global optimization problems. It adopts the stochastic searching method to make selection of the parents in the mutation operator, which benefits the search of global optimization value. However, the selection method reveals the convergence in low speed. So for the sake of better convergence pe...

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...

A new version of differential evolution (DE) algorithm, in which immune concepts and methods are applied to determine the parameter setting, named immune self-adaptive differential evolution (ISDE), is proposed to improve the performance of the DE algorithm. During the actual operation, ISDE seeks the optimal parameters arising from the evolutionary process, which enable ISDE to alter the algor...

limited water resources, especially in iran, require most effective method of water use to avoid losses. one of the important parameters in irrigation is water distribution uniformity coefficient (cu) in sprinkler irrigation. cu in every irrigation depends on pressure heads (p), riser height (rh), distance between sprinklers on lateral pipes (sl), and the distance between lateral pipes (sm). th...