Journal ArticleDOI
A comparative study of Artificial Bee Colony algorithm
Dervis Karaboga,Bahriye Akay +1 more
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Results show that the performance of the ABC is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters.About:
This article is published in Applied Mathematics and Computation.The article was published on 2009-08-01. It has received 2835 citations till now. The article focuses on the topics: Artificial bee colony algorithm & Meta-optimization.read more
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Integrating the artificial bee colony and bees algorithm to face constrained optimization problems
TL;DR: Experimental results demonstrate that the performance of the ABC-BA approximates or exceeds the winner of either ABC or BA, and theABC-BA is recommended as an alternative to ABC and BA for handling COPs.
Journal ArticleDOI
Hybrid teaching–learning-based optimization of correlation-aware service composition in cloud manufacturing
Jiajun Zhou,Xifan Yao +1 more
TL;DR: This study proposes a new hybrid teaching–learning-based optimization (HTLBO) algorithm for optimal service composition with the consideration of service correlations and conducts experiments to verify the effectiveness and feasibility of the proposed algorithm.
Journal ArticleDOI
Solutions of Time-Invariant Spatial Focusing for Multi-Targets Using Time Modulated Frequency Diverse Antenna Arrays
A-Min Yao,Wen Wu,Da-Gang Fang +2 more
TL;DR: In this article, two classes of time-modulated frequency diverse arrays for achieving time-invariant spatial focusing for multiple targets are proposed, which are termed TMOFO-based phase conjugating array (TMOFO)-PCA, which is based on modified timemodulated optimized frequency offset, that depends only on the range.
Journal ArticleDOI
Simultaneous identification of structural parameters and dynamic input with incomplete output‐only measurements
Hao Sun,Raimondo Betti +1 more
TL;DR: In this article, a hybrid heuristic optimization strategy is presented to simultaneously identify structural parameters and, when possible, dynamic input time histories from incomplete sets of output measurements, combining a swarm intelligence algorithm, the artificial bee colony algorithm, with a local search operator, Nelder-Mead simplex method, integrated in a search space reduction approach, so as to improve the convergence efficiency of the overall identification process.
Journal ArticleDOI
Multi-objective hybrid artificial bee colony algorithm enhanced with Lévy flight and self-adaption for cloud manufacturing service composition
Jiajun Zhou,Xifan Yao +1 more
TL;DR: A multi-objective hybrid artificial bee colony (HABC) algorithm to address the SCOS problem in consideration of both quality of service (QoS) and energy consumption is proposed, to which an improved solution update equation with multiple dimensions of perturbation was adopted in the employed bee phase.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Journal ArticleDOI
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.
Book
Self-Organizing Maps
TL;DR: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981 as mentioned in this paper, and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it.
Book
Genetic Algorithms + Data Structures = Evolution Programs
TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.