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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A new technique based on Artificial Bee Colony Algorithm for optimal sizing of stand-alone photovoltaic system
TL;DR: The proposed methodology is applied to optimize the cost of the PV system including photovoltaic, a battery bank, a Battery charger controller, and inverter, and it is found that the ABC is more optimal than GA.
Journal ArticleDOI
Portfolio optimization using novel co-variance guided Artificial Bee Colony algorithm
Divya Kumar,Krishn Kumar Mishra +1 more
TL;DR: A novel co-variance guided Artificial Bee Colony algorithm named as Multi-objective Co-Variance based ABC (M-CABC) is presented, which validates the adept performance of the proposed algorithm in finding various optimal trade-off solutions simultaneously handling realistic constraints.
Journal ArticleDOI
A modified artificial bee colony algorithm for order acceptance in two-machine flow shops
TL;DR: A modified artificial bee colony algorithm is developed that is able to generate good solutions for large-scale problem instances and is shown to be an effective evolutionary algorithm to handle combinatorial optimization problems.
Journal ArticleDOI
An improved global-best harmony search algorithm for faster optimization
TL;DR: Experimental results tested on twenty-eight benchmark functions indicate that IGHS is far better than basic harmony search (HS) algorithm and GHS, and has also been compared with other eight well known metaheuristics.
Journal ArticleDOI
A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions
TL;DR: In this paper, a review on the three most important communication techniques (ground, aerial, and underwater vehicles) has been presented that throws light on trajectory planning, its optimization, and various issues in a summarized way.
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.