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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High dimensional real parameter optimization with teaching learning based optimization
TL;DR: Simulation of TLBO for high dimensional benchmark function optimizations and compare its results with very widely used alternate techniques reveal that TLBO is able to address the computational cost issue for all simulated functions to a large dimensions compared to other two techniques.
Journal ArticleDOI
A review of applications of animal-inspired evolutionary algorithms in reservoir operation modelling
TL;DR: Comparison results revealed that constrained, discrete and randomized varieties of the animal‐inspired EAs outperformed unconstrained, continuous and deterministic varieties, respectively because of larger feasible search space, better solution quality and shorter computational time.
Journal ArticleDOI
A novel non-Lyapunov approach through artificial bee colony algorithm for detecting unstable periodic orbits with high orders
TL;DR: Simulation results show that ABC is superior to QPSO, and it is a successful method in detecting the UPOs, with the advantages of fast convergence, high precision and robustness.
Journal ArticleDOI
Pilot Tones Optimization Using Artificial Bee Colony Algorithm for MIMO---OFDM Systems
TL;DR: The results show that designing pilot tones using the ABC algorithm outperforms other considered placement strategies in terms of high system performance and low computational complexity.
Journal ArticleDOI
A Survey of Trajectory Planning Techniques for Autonomous Systems
Imran Mir,Faiza Gul,Suleman Mir,Mansoor A. Khan,Nasir Saeed,Laith Abualigah,Belal Abuhaija,Amir H. Gandomi +7 more
TL;DR: This work offers an overview of the effective communication techniques for space exploration of ground, aerial, and underwater vehicles, and addresses numerical, bio-inspired, and hybrid methodologies for each dimension described.
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.