Book Chapter10.1007/978-3-642-17537-4_71
A three-strategy based differential evolution algorithm for constrained optimization
Saber M. Elsayed,Ruhul A. Sarker,Daryl Essam +2 more
- 22 Nov 2010
- pp 585-592
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TL;DR: SAOMDE is proposed, a Self-Adaptive Operator Mix Differential Evolution algorithm, indicated as SAOMDE, for solving a variety of COPs, which utilizes the strengths of three well-known DE variants through an adaptive learning process.
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Abstract: Constrained Optimization is one of the most active research areas in the computer science, operation research and optimization fields. The Differential Evolution (DE) algorithm is widely used for solving continuous optimization problems. However, no single DE algorithm performs consistently over a range of Constrained Optimization Problems (COPs). In this research, we propose a Self-Adaptive Operator Mix Differential Evolution algorithm, indicated as SAOMDE, for solving a variety of COPs. SAOMDE utilizes the strengths of three well-known DE variants through an adaptive learning process. SAOMDE is tested by solving 13 test problems. The results showed that SAOMDE is not only superior to three single mutation based DE, but also better than the state-of-the-art algorithms.
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Citations
Multi-operator based evolutionary algorithms for solving constrained optimization problems
TL;DR: An algorithm framework that uses multiple search operators in each generation, which demonstrated that both GA and DE based algorithms show competitive, if not better, performance as compared to the state of the art algorithms.
186
Differential evolution algorithm with multi-population cooperation and multi-strategy integration
TL;DR: The research results reveal that the performance of MPEDE may be improved by adding an information sharing mechanism and modifying the grouping mechanism, and the experimental results show that thePerformance of the MPMSDE algorithm is very competitive in calculation accuracy and convergence speed.
64
Differential evolution with mixed mutation strategy based on deep reinforcement learning
Zhiping Tan,Kangshun Li +1 more
TL;DR: A mixed mutation strategy DE algorithm based on deep Q-network (DQN), named DEDQN is proposed in this paper, in which a deep reinforcement learning approach realizes the adaptive selection of mutation strategy in the evolution process.
56
Integrated strategies differential evolution algorithm with a local search for constrained optimization
Saber M. Elsayed,Ruhul A. Sarker,Daryl Essam +2 more
- 05 Jun 2011
TL;DR: A DE algorithm that uses multiple search operators and constraint handling techniques, and the results showed that the proposed algorithm is superior to state of the art algorithms.
22
Differential evolution combined with constraint consensus for constrained optimization
Noha M. Hamza,Saber M. Elsayed,Daryl Essam,Ruhul A. Sarker +3 more
- 05 Jun 2011
TL;DR: This paper introduces a Constraint Consensus (CC) method within the Differential Evolution (DE) algorithm for solving COPs, and shows that the solutions are competitive, if not better, as compared to the state of the art algorithms.
15
References
•Book
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Kenneth Price,Rainer Storn,Jouni Lampinen +2 more
- 13 Dec 2005
TL;DR: This volume explores the differential evolution (DE) algorithm in both principle and practice and is a valuable resource for professionals needing a proven optimizer and for students wanting an evolutionary perspective on global numerical optimization.
5.9K
•Book
Differential Evolution: A Practical Approach to Global Optimization
Kenneth Price,Rainer Storn,Jouni Lampinen +2 more
- 25 Nov 2014
TL;DR: The differential evolution (DE) algorithm is a practical approach to global numerical optimization which is easy to understand, simple to implement, reliable, and fast as discussed by the authors, which is a valuable resource for professionals needing a proven optimizer and for students wanting an evolutionary perspective on global numerical optimisation.
5.6K
Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces
Kenneth Price
- 01 Jan 2004
TL;DR: A new heuristic approach for minimizing possibly nonlinear and non differentiable continuous space functions is presented and it will be demonstrated that the new method converges faster and with more certainty than Adaptive Simulated Annealing as well as the Annealed Nelder&Mead approach.
4.3K
Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization
TL;DR: This paper proposes a self- Adaptive DE (SaDE) algorithm, in which both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions.
3.6K