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
Citations
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Journal ArticleDOI
An artificial bee colony-based hybrid approach for waste collection problem with midway disposal pattern
TL;DR: The proposed hybrid ABC algorithm exhibits a better optimum-seeking performance than four popular metaheuristics, namely a genetic algorithm, a particle swarm optimization algorithm, an enhanced ABC algorithm and a hybrid particle Swarm optimization algorithm.
Journal ArticleDOI
A novel accelerated artificial bee colony algorithm for optimal design of two dimensional FIR filter
TL;DR: A novel approach for the design of two-dimensional (2D) Finite Impulse Response (FIR) filters by adopting multiple parameters change of search equation at each step and introducing a change during the initialization strategy of scout bees in the proposed AABC algorithm.
Journal ArticleDOI
A modified artificial bee colony algorithm for constrained optimization problems
TL;DR: This paper describes a modified ABC algorithm for constrained optimization problems and compares the performance of the modifiedABC algorithm against those of state-of-the-art algorithms for a set of constrained test problems.
Journal ArticleDOI
On the performance of particle swarm optimisation without some control parameters for global optimisation
TL;DR: This paper establishes that the basic particle swarm optimisation BPSO technique can perform efficiently without some or any of the control parameters in the particle velocity update formula and presents a modified B PSO M-BPSO that ameliorate the problem of premature convergence associated with PSO when optimising high dimensional multi-modal problems.
Proceedings ArticleDOI
Artificial Bee Colony for workflow scheduling
TL;DR: This research integrates the concept of project scheduling with the workflow scheduling problem to formulate a mathematical model, which expects to minimize the total completion time, and shows that ABC can be considered a practical method for complicated workflow scheduling problems in the cloud computing environment.
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.