A multi-objective Artificial Bee Colony for optimizing multi-objective problems

Hedayatzadeh, Ramin; Hasanizadeh, Bahareh; Akbari, Reza; Ziarati, Koorush

doi:10.1109/icacte.2010.5579761

Cited by 46 publications

(37 citation statements)

References 9 publications

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“…The ABC algorithm's advantages of great accuracy and satisfactory convergence speed make it suitable for the multi-objective optimization problems. Hedayatzadeh et al designed a multi-objective artificial bee colony (MOABC) based on the Pareto theory and ε-domination notion in [18]. The performance of a Pareto-based MOABC algorithm has been investigated by Akbari et al on CEC'09 data sets in [19] and their studies showed that the algorithm could provide competitive performance.…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved Multi-Objective Artificial Bee Colony Optimization Algorithm with Regulation Operators

Huo

Liu

2017

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To achieve effective and accurate optimization for multi-objective optimization problems, a multi-objective artificial bee colony algorithm with regulation operators (RMOABC) inspired by the intelligent foraging behavior of honey bees was proposed in this paper. The proposed algorithm utilizes the Pareto dominance theory and takes advantage of adaptive grid and regulation operator mechanisms. The adaptive grid technique is used to adaptively assess the Pareto front maintained in an external archive and the regulation operator is used to balance the weights of the local search and the global search in the evolution of the algorithm. The performance of RMOABC was evaluated in comparison with other nature inspired algorithms includes NSGA-II and MOEA/D. The experiments results demonstrated that the RMOABC approach has better accuracy and minimal execution time.

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Section: Literature Reviewmentioning

confidence: 99%

An Improved Multi-Objective Artificial Bee Colony Optimization Algorithm with Regulation Operators

Huo

Liu

2017

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“…To fully evaluate the performance of the proposed M 2 OBA algorithm, three successful nature-inspired multi-objective optimization algorithms were used for comparison: the nondominated sorting genetic algorithm II (NSGA-II) [30]; the multi-objective particle swarm optimization (MOPSO) [32]; the multi-objective artificial bee colony algorithm (MOABC) [29].…”

Section: Experimental Settingmentioning

confidence: 99%

“…Although ABC is relatively new, its relative simplicity and population-based feature have made it a high competitor in solving the MO problems. Several existing multi-objective ABC (MOABC) algorithms can be found in [28,29]. However, compare to the huge in-depth studies of other EA and SI algorithms, such as nondominated sorting genetic algorithm II (NSGAII) [30], strength Pareto evolutionary algorithm (SPEA2) [31], and multi-objective particle swarm optimization (MOPSO) [32], how to improve the diversity of swarm or overcome the local convergence of MOABC is still a challenging to the MO researchers.…”

Section: Introductionmentioning

confidence: 99%

Multi-hive bee foraging algorithm for multi-objective optimal power flow considering the cost, loss, and emission

Chen

Zhu

2014

International Journal of Electrical Power & Energy Systems

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“…ABC [62] works on the foraging behavior of a honey bee and TLBO [148] works on the philosophy of teaching and learning process. Oppositionbased learning is a fast growing area of research developed in [172].…”

Section: Decomposition Based Multi-objective Evolutionary Algorithmmentioning

confidence: 99%

Evolutionary Algorithms Based on Decomposition and Indicator Functions: State-of-the-art Survey

Khan¹,

Salhi²,

Jan³

et al. 2016

ijacsa

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Abstract-In the last two decades, multiobjective optimization has become mainstream because of its wide applicability in a variety of areas such engineering, management, the military and other fields. Multi-Objective Evolutionary Algorithms (MOEAs) play a dominant role in solving problems with multiple conflicting objective functions. They aim at finding a set of representative Pareto optimal solutions in a single run. Classical MOEAs are broadly in three main groups: the Pareto dominance based MOEAs, the Indicator based MOEAs and the decomposition based MOEAs. Those based on decomposition and indicator functions have shown high search abilities as compared to the Pareto dominance based ones. That is possibly due to their firm theoretical background. This paper presents state-of-the-art MOEAs that employ decomposition and indicator functions as fitness evaluation techniques along with other efficient techniques including those which use preference based information, local search optimizers, multiple ensemble search operators together with self-adaptive strategies, metaheuristics, mating restriction approaches, statistical sampling techniques, integration of Fuzzy dominance concepts and many other advanced techniques for dealing with diverse optimization and search problems

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A multi-objective Artificial Bee Colony for optimizing multi-objective problems

Cited by 46 publications

References 9 publications

An Improved Multi-Objective Artificial Bee Colony Optimization Algorithm with Regulation Operators

An Improved Multi-Objective Artificial Bee Colony Optimization Algorithm with Regulation Operators

Multi-hive bee foraging algorithm for multi-objective optimal power flow considering the cost, loss, and emission

Evolutionary Algorithms Based on Decomposition and Indicator Functions: State-of-the-art Survey

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