Proceedings Article10.1109/IC3A48958.2020.233276
An improved Differential Evolution Algorithm with Self Adaptive Mutation Strategies for Global Optimization
Sunil Kumar Gouda,Ashok Kumar Mehta +1 more
- 01 Feb 2020
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TL;DR: New self-adaptive mutation strategies with booster vector to improve global optimization of DE/rand/1/bin and DE/best/ 1/bin is proposed and verifies that proposed self- Adaptive strategies outperformed the competitors.
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Abstract: To solve real-world global optimization problem differential evolution algorithm is used as one of the best nature influenced algorithm. The use of different effective mutation strategies and proper selection of effective control parameters directly affect the performance and convergent rate of differential evolution method. Although its performance is very good but suffers from population diversity and stagnation.In this paper, new self-adaptive mutation strategies with booster vector to improve global optimization of DE/rand/1/bin and DE/best/1/bin is proposed. Elite archive strategies with dynamic adjustment of control parameter with booster vector added to afford more bandwidth for electing an effective mutant solution. The proposed algorithm is compared with five DE and six non-DE algorithms by using a set of twenty benchmark functions on COCO (comparing Continuous Optimizers) framework. The experimental result verifies that proposed self-adaptive strategies outperformed the competitors.
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References
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
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.
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Genetic Algorithms and Machine Learning
TL;DR: There is no a priori reason why machine learning must borrow from nature, but many machine learning systems now borrow heavily from current thinking in cognitive science, and rekindled interest in neural networks and connectionism is evidence of serious mechanistic and philosophical currents running through the field.
JADE: Adaptive Differential Evolution With Optional External Archive
Jingqiao Zhang,A.C. Sanderson +1 more
TL;DR: Simulation results show that JADE is better than, or at least comparable to, other classic or adaptive DE algorithms, the canonical particle swarm optimization, and other evolutionary algorithms from the literature in terms of convergence performance for a set of 20 benchmark problems.
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Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems
TL;DR: The results show that the algorithm with self-adaptive control parameter settings is better than, or at least comparable to, the standard DE algorithm and evolutionary algorithms from literature when considering the quality of the solutions obtained.
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