Journal ArticleDOI
Hybridizing artificial bee colony with monarch butterfly optimization for numerical optimization problems
TLDR
The experimental results show that the proposed HAM algorithm is clearly superior to the standard ABC and MBO algorithms, as well as to other well-known algorithms, in terms of achieving the best optimal value and convergence speed.Abstract:
The aim of the study was to propose a new metaheuristic algorithm that combines parts of the well-known artificial bee colony (ABC) optimization with elements from the recent monarch butterfly optimization (MBO) algorithm. The idea is to improve the balance between the characteristics of exploration and exploitation in those algorithms in order to address the issues of trapping in local optimal solution, slow convergence, and low accuracy in numerical optimization problems. This article introduces a new hybrid approach by modifying the butterfly adjusting operator in MBO algorithm and uses that as a mutation operator to replace employee phase of the ABC algorithm. The new algorithm is called Hybrid ABC/MBO (HAM). The HAM algorithm is basically employed to boost the exploration versus exploitation balance of the original algorithms, by increasing the diversity of the ABC search process using a modified operator from MBO algorithm. The resultant design contains three components: The first and third component implements global search, while the second one performs local search. The proposed algorithm was evaluated using 13 benchmark functions and compared with the performance of nine metaheuristic methods from swarm intelligence and evolutionary computing: ABC, MBO, ACO, PSO, GA, DE, ES, PBIL, and STUDGA. The experimental results show that the HAM algorithm is clearly superior to the standard ABC and MBO algorithms, as well as to other well-known algorithms, in terms of achieving the best optimal value and convergence speed. The proposed HAM algorithm is a promising metaheuristic technique to be added to the repertory of optimization techniques at the disposal of researchers. The next step is to look into application fields for HAM.read more
Citations
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Journal ArticleDOI
Monarch butterfly optimization: A comprehensive review
TL;DR: This review study serves as a solid reference for future studies in the arena of SI and in particular the MBO algorithm including its modifications, hybridizations, variants, and applications.
Journal ArticleDOI
The monarch butterfly optimization algorithm for solving feature selection problems
Mohammed Alweshah,Saleh Al khalaileh,Brij B. Gupta,Brij B. Gupta,Ammar Almomani,Abdelaziz I. Hammouri,Mohammed Azmi Al-Betar +6 more
TL;DR: The use of the MBO to solve the FS problems has been proven through the results obtained to be effective and highly efficient in this field, and the results have also proven the strength of the balance between global and local search of MBO.
Journal ArticleDOI
Multi-strategy monarch butterfly optimization algorithm for discounted {0-1} knapsack problem
TL;DR: In this paper, a novel multi-strategy monarch butterfly optimization (MMBO) algorithm for DKP is proposed and two effective strategies, neighborhood mutation with crowding and Gaussian perturbation, are introduced into MMBO.
Journal ArticleDOI
Monarch Butterfly Optimization Based Convolutional Neural Network Design
TL;DR: Experimental results proved that the proposed hybridized monarch butterfly optimization algorithm managed to obtain higher classification accuracy than other approaches, the results of which were published in the modern computer science literature.
Journal ArticleDOI
A Cognitively Inspired Hybridization of Artificial Bee Colony and Dragonfly Algorithms for Training Multi-layer Perceptrons
TL;DR: Experimental results show that HAD algorithm is clearly superior to the standard ABC and DA algorithms, as well as to other well-known algorithms, in terms of achieving the best optimal value, convergence speed, avoiding local minima and accuracy of trained MLPs.
References
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Book
Convex Optimization
Stephen Boyd,Lieven Vandenberghe +1 more
TL;DR: In this article, the focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them, and a comprehensive introduction to the subject is given. But the focus of this book is not on the optimization problem itself, but on the problem of finding the appropriate technique to solve it.
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.
Proceedings ArticleDOI
A new optimizer using particle swarm theory
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
Journal ArticleDOI
Grey Wolf Optimizer
TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.
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.