Artificial Bee Colony (ABC) Algorithm for Constrained Optimization Improved with Genetic Operators
Nebojsa Bacanin,Milan Tuba +1 more
TLDR
Modifications to the ABC algorithm for constrained optimization problems that improve performance of the algorithm are introduced based on genetic algorithm (GA) operators and are applied to the creation of new candidate solutions.Abstract:
Artificial bee colony (ABC) is a relatively new swarm intelligence based metaheuristic. It was successfully applied to unconstrained optimization problems and later it was adjusted for constrained problems as well. In this paper we introduce modifications to the ABC algorithm for constrained optimization problems that improve performance of the algorithm. Modifications are based on genetic algorithm (GA) operators and are applied to the creation of new candidate solutions. We implemented our modified algorithm and tested it on 13 standard benchmark functions. The results were compared to the results of the latest (2011) Karaboga and Akay’s ABC algorithm and other state-of-the-art algorithms where our modified algorithm showed improved performance considering best solutions and even more consideringread more
Citations
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A quick artificial bee colony (qABC) algorithm and its performance on optimization problems
Dervis Karaboga,Beyza Gorkemli +1 more
TL;DR: Quick artificial bee colony (qABC) is a new version of ABC algorithm which models the behaviour of onlooker bees more accurately and improves the performance of standard ABC in terms of local search ability.
Journal ArticleDOI
Genetic Bee Colony (GBC) algorithm
TL;DR: The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes, which proves that the GBC algorithms is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification.
Journal ArticleDOI
COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach
Miodrag Zivkovic,Nebojsa Bacanin,K. Venkatachalam,Anand Nayyar,Aleksandar Djordjevic,Ivana Strumberger,Fadi Al-Turjman +6 more
TL;DR: Wang et al. as mentioned in this paper proposed a hybrid approach between machine learning, adaptive neuro-fuzzy inference system and enhanced beetle antennae search swarm intelligence metaheuristics to predict the number of the COVID-19 cases.
Journal ArticleDOI
Improved bat algorithm applied to multilevel image thresholding.
Adis Alihodzic,Milan Tuba +1 more
TL;DR: The new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed.
BookDOI
Cuckoo Search and Firefly Algorithm
TL;DR: This chapter provides an overview of both cuckoo search and firefly algorithm as well as their latest developments and applications and analyzes these algorithms to gain insight into their search mechanisms and find out why they are efficient.
References
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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.
Journal ArticleDOI
An efficient constraint handling method for genetic algorithms
TL;DR: GA's population-based approach and ability to make pair-wise comparison in tournament selection operator are exploited to devise a penalty function approach that does not require any penalty parameter to guide the search towards the constrained optimum.
Journal ArticleDOI
On the performance of artificial bee colony (ABC) algorithm
Dervis Karaboga,Bahriye Basturk +1 more
TL;DR: The simulation results show that the performance of ABC algorithm is comparable to those of differential evolution, particle swarm optimization and evolutionary algorithm and can be efficiently employed to solve engineering problems with high dimensionality.
Journal ArticleDOI
Stochastic ranking for constrained evolutionary optimization
Thomas Philip Runarsson,Xin Yao +1 more
TL;DR: A novel approach to balance objective and penalty functions stochastically, i.e., stochastic ranking, is introduced, and a new view on penalty function methods in terms of the dominance of penalty and objective functions is presented.