Journal Article10.1016/J.NEUCOM.2011.11.033
A novel modified binary differential evolution algorithm and its applications
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TL;DR: A novel modified binary differential evolution algorithm (NMBDE) inspired by the concept of Estimation of Distribution Algorithm and DE is proposed, which can efficiently maintain diversity of population and achieve a better tradeoff between the exploration and exploitation capabilities by cooperating with the selection operator.
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About: This article is published in Neurocomputing. The article was published on 01 Dec 2012. The article focuses on the topics: Meta-optimization & Multi-swarm optimization.
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Citations
Recent advances in differential evolution – An updated survey
TL;DR: It is found that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research on DE.
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Choosing DBSCAN Parameters Automatically using Differential Evolution
Amin Karami,Ronnie Johansson +1 more
TL;DR: An efficient and effective hybrid clustering method, named BDE-DBSCAN, that combines Binary Differential Evolution and DBSCAN algorithm to simultaneously quickly and automatically specify appropriate parameter values for Eps and MinPts is presented.
A New Binary Particle Swarm Optimization Approach: Momentum and Dynamic Balance Between Exploration and Exploitation
TL;DR: This article proposes a new algorithm called dynamic sticky binary PSO by developing a dynamic parameter control strategy based on an investigation of exploration and exploitation in the binary search spaces to evolve better solutions for binary problems.
110
A human learning optimization algorithm and its application to multi-dimensional knapsack problems
Ling Wang,Ruixin Yang,Haoqi Ni,Wei Ye,Minrui Fei,Panos M. Pardalos +5 more
- 01 Sep 2015
TL;DR: Four learning operators inspired by the human learning process are developed and the presented HLO achieves the best performance in comparison with other meta-heuristics, which demonstrates that HLO is a promising optimization tool.
75
SFE: A Simple, Fast and Efficient Feature Selection Algorithm for High-Dimensional Data
Behrouz Ahadzadeh,Moloud Abdar,Fatemeh Safara,Abbas Khosravi,Mohammad Bagher Menhaj,Ponnuthurai Nagaratnam Suganthan +5 more
TL;DR: In this paper , a feature selection algorithm, called Simple, Fast, and Efficient (SFE), is proposed for high-dimensional datasets, which performs its search process using a search agent and two operators: non-selection and selection.
References
Differential Evolution: A Survey of the State-of-the-Art
TL;DR: A detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far are presented.
A discrete binary version of the particle swarm algorithm
James Kennedy,Russell C. Eberhart +1 more
- 12 Oct 1997
TL;DR: The paper reports a reworking of the particle swarm algorithm to operate on discrete binary variables, where trajectories are changes in the probability that a coordinate will take on a zero or one value.
5K
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
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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