We propose a modified version of the differential evolution approach to solve engineering design problems. The aim is to allow each parent in the population to generate more than one offspring at each generation and therefore, to increase its probability of generating a better offspring. To deal with constraints, we use some criteria based on feasibility and a diversity mechanism to maintain infeasible solutions in the population. The approach is tested against penalty function approaches and its performance is also compared against state-of-the-art approaches
In recent years, Differential Evolution (DE) has shown excellent performance in solving optimization...
In the present study a modified new variant of Differential Evolution (DE) is proposed, named Cultiv...
Purpose \u2013 The purpose of this paper is to show, on a widely used benchmark problem, that adapti...
In this paper, we incorporate a diversity mechanism to the differential evolution algorithm to solve...
Differential evolution (DE) algorithm has been shown to be a simple and efficient evolutionary algor...
As a relatively new population-based optimization technique, differential evolution has been attract...
Engineering design optimization problems are formulated as large-scale mathematical programming prob...
The advance of the computational resources has encouraged the utilization of optimization techniques...
Several constrained and unconstrained optimization problems have been adequately solved over the yea...
The Differential Evolution algorithm, like other evolutionary techniques, presents as main disadvant...
In past, only a few attempts have been made in adopting a unified outlook towards different paradigm...
Solving many real-life engineering problems requires often global and efficient (in terms of objecti...
<div><p>Abstract The basic information required to utilize one of possible computation tools/algori...
Preserving an appropriate population diversity is critical for the performance of evolutionary algor...
Many improved differential Evolution (DE) algorithms have emerged as a very competitive class of evo...
In recent years, Differential Evolution (DE) has shown excellent performance in solving optimization...
In the present study a modified new variant of Differential Evolution (DE) is proposed, named Cultiv...
Purpose \u2013 The purpose of this paper is to show, on a widely used benchmark problem, that adapti...
In this paper, we incorporate a diversity mechanism to the differential evolution algorithm to solve...
Differential evolution (DE) algorithm has been shown to be a simple and efficient evolutionary algor...
As a relatively new population-based optimization technique, differential evolution has been attract...
Engineering design optimization problems are formulated as large-scale mathematical programming prob...
The advance of the computational resources has encouraged the utilization of optimization techniques...
Several constrained and unconstrained optimization problems have been adequately solved over the yea...
The Differential Evolution algorithm, like other evolutionary techniques, presents as main disadvant...
In past, only a few attempts have been made in adopting a unified outlook towards different paradigm...
Solving many real-life engineering problems requires often global and efficient (in terms of objecti...
<div><p>Abstract The basic information required to utilize one of possible computation tools/algori...
Preserving an appropriate population diversity is critical for the performance of evolutionary algor...
Many improved differential Evolution (DE) algorithms have emerged as a very competitive class of evo...
In recent years, Differential Evolution (DE) has shown excellent performance in solving optimization...
In the present study a modified new variant of Differential Evolution (DE) is proposed, named Cultiv...
Purpose \u2013 The purpose of this paper is to show, on a widely used benchmark problem, that adapti...