Results 261 to 270 of about 117,773 (298)

Improving differential evolution through a unified approach

Journal of Global Optimization, 2012
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Nikhil Padhye, Piyush Bhardawaj, Kalyanmoy Deb   +2 more
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Improved differential evolution algorithm with decentralisation of population

International Journal of Bio-Inspired Computation, 2011
Differential evolution (DE) is a reliable and versatile function optimiser especially suited for continuous optimisation problems. Practical experience, however, shows that DE easily looses diversity and is susceptible to premature and/or slow convergence.
Musrrat Ali, Millie Pant, Ajith Abraham
openaire   +1 more source

Improving Modified Differential Evolution for Fuzzy Clustering

2018
Differential evolution is a real value encoded evolutionary algorithm for global optimization. It has gained popularity due to its simplicity and efficiency. Use of special kind of mutation and crossover operators differentiates it from other evolutionary algorithms.
Jnanendra Prasad Sarkar, Indrajit Saha, Anasua Sarkar, Ujjwal Maulik   +3 more
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Improving differential evolution algorithm by synergizing different improvement mechanisms

ACM Transactions on Autonomous and Adaptive Systems, 2012
Differential Evolution (DE) is a well-known Evolutionary Algorithm (EA) for solving global optimization problems. Practical experiences, however, show that DE is vulnerable to problems like slow and/or premature convergence. In this article we propose a simple and modified DE framework, called MDE, which is a fusion of three recent modifications in DE:
Musrrat Ali, Millie Pant, Ajith Abraham
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An Improved Differential Evolution Alogorithm for Optimization

2009 IITA International Conference on Control, Automation and Systems Engineering (case 2009), 2009
Differential Evolution (DE) is an efficient approach capable of handling non-differentiable, non-linear and multi-model objective functions. However, in convergence speed and global optimization, there is still much room for DE to be improved. In this paper, double best mutation operation and chaos Differential Evolution are proposed to improve DE ...
Jin Huibin, Liu Mingguang
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Differential Evolution Based on Improved Learning Strategy

2008
From a learning perspective, the mutation scheme in differential evolution (DE) can be regarded as a learning strategy. When mutating, three random individuals are selected and placed in a random order. This strategy, however, probably suffers some drawbacks which can slow down the convergence rate.
Yuan Shi, Zhen-zhong Lan, Xiang-hu Feng
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Improved Differential Evolution with Local Search

Journal of Convergence Information Technology, 2012
Differential evolution (DE) is a popular meta-heuristic optimizer which has shown good performance in solving many real-life and benchmark optimization problems. However, DE usually shows slow convergence rate at the last stage of the evolution.
He Li -, Jun Tang -
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Improving Differential Evolution by Altering Steps in EC

2010
In past, only a few attempts have been made in adopting a unified outlook towards different paradigms in Evolutionary Computation. The underlying motivation of these studies was aimed at gaining better understanding of evolutionary methods, both at the level of theory as well as application, in order to design efficient evolutionary algorithms for ...
Nikhil Padhye, Piyush Bhardawaj, Kalyanmoy Deb   +2 more
openaire   +1 more source

Improved differential evolution for global optimization

2010 2nd IEEE International Conference on Information Management and Engineering, 2010
Differential Evolution (DE) is a recently proposed population based evolutionary technique, which attracts much attention for its simple concept, easy implementation and robustness. In order to enhance the performance of classical DE, this paper presents an improved DE algorithm for global optimization.
Jiahua Xie, Jie Yang
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Improving performance in distributed embodied evolution: Distributed Differential Embodied Evolution

The 2018 Conference on Artificial Life, 2018
The field of Embodied Evolution has been strongly developing during the last ten years by more than doubling the yearly number of contributions since 2008 (Bredeche et al., 2018).
Pedro Trueba, Abraham Prieto
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