Journal ArticleDOI
A modified artificial bee colony algorithm
Weifeng Gao,Sanyang Liu +1 more
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TLDR
An improved solution search equation is proposed, which is based on that the bee searches only around the best solution of the previous iteration to improve the exploitation and makes up the modified ABC, which excludes the probabilistic selection scheme and scout bee phase.About:
This article is published in Computers & Operations Research.The article was published on 2012-03-01. It has received 526 citations till now. The article focuses on the topics: Artificial bee colony algorithm & Differential evolution.read more
Citations
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Journal ArticleDOI
A comprehensive survey: artificial bee colony (ABC) algorithm and applications
TL;DR: This work presents a comprehensive survey of the advances with ABC and its applications and it is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.
Journal ArticleDOI
A survey on new generation metaheuristic algorithms
TL;DR: In this survey, fourteen new and outstanding metaheuristics that have been introduced for the last twenty years other than the classical ones such as genetic, particle swarm, and tabu search are distinguished.
Journal ArticleDOI
Bio inspired computing - A review of algorithms and scope of applications
TL;DR: This review identifies the popularly used algorithms within the domain of bio-inspired algorithms and discusses their principles, developments and scope of application, which would pave the path for future studies to choose algorithms based on fitment.
Journal ArticleDOI
A global best artificial bee colony algorithm for global optimization
TL;DR: A modified ABC algorithm (denoted as ABC/best), which is based on that each bee searches only around the best solution of the previous iteration in order to improve the exploitation, is proposed.
Journal ArticleDOI
A self-adaptive artificial bee colony algorithm based on global best for global optimization
TL;DR: A self-adaptive ABC algorithm based on the global best candidate (SABC-GB) for global optimization that is superior to the other algorithms for solving complex optimization problems and validated in real-world application.
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.
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
Ant Colony Optimization
TL;DR: Ant colony optimization (ACO) is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals as discussed by the authors In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization.
Journal ArticleDOI
A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm
Dervis Karaboga,Bahriye Basturk +1 more
TL;DR: Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm that is used for optimizing multivariable functions and the results showed that ABC outperforms the other algorithms.
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Gbest-guided artificial bee colony algorithm for numerical function optimization
Guopu Zhu,Sam Kwong +1 more