Constrained artificial bee colony algorithm for optimization problems

Babaeizadeh, Soudeh; Ahmad, Rohanin

doi:10.1063/1.4954521

Cited by 9 publications

(12 citation statements)

References 16 publications

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“…Some scholars have put forward some improvements, which mainly involve the enhancement of the initial solution, selection strategy, update strategy [24,25], operation mode [26,27], and hybrid algorithm [28]. Because the ABC algorithm is a kind of unconstrained optimization algorithm, some scholars have applied it to the constrained optimization problem [28,29] and multiobjective optimization problem [30][31][32][33], for the search strategy formula of basic ABC algorithm; that is, only one individual and one dimension are selected randomly at each update. This paper uses the evolutionary ideas of particle swarm optimization (PSO), combining with the current location [34], individual best value, and global optimal value, and introduces the linearly decreasing inertia weight.…”

Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

Research on Dynamic Facility Layout Problem of Manufacturing Unit Considering Human Factors

Tan

2018

Mathematical Problems in Engineering

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As many said, industry 4.0 is an epoch-making revolution which brought the manufacturing market much faster changes and severer competitions. As an important part of the manufacturing system, facility layout has direct impact on business benefit; at the same time, despite the intelligent factory, intelligent production has its own characteristics. However, there is one point on which industry and academia have basically formed a consensus: it is not true that industry 4.0 does not need human beings; on the contrary, human initiative plays an unabated role in the development of industry 4.0. This paper will focus on the dynamic facility layout of the manufacturing unit. Based on the system above and the traditional optimization model, a mathematic model is built to find the best solution combining safety, sustainability, high efficiency, and low cost. And penalty function with adaptive penalty factor and advanced artificial bee colony algorithm is used to solve the constrained model. In the end, by studying few cases, the model is proved to be effective in both efficiency improvement and the implementation of safe and comfort human-machine interaction.

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Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

Research on Dynamic Facility Layout Problem of Manufacturing Unit Considering Human Factors

Tan

2018

Mathematical Problems in Engineering

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“…Constrained optimization problems [30] are discussed by Soudeh et al [13]. In order to overcome the problem of insufficiencies in ABC algorithm author has developed an efficient constrained artificial bee colony algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…The standard ABC algorithm [24] had disadvantages of hastily falling into local optima and slow convergence rate in later stage [10], [13], and [14]. So that we are using random walk step in this place.…”

Section: Procedurementioning

confidence: 99%

A Method of A-BAT Algorithm Based Query Optimization for Crowd Sourcing System

Cincy¹,

Jeba²

2018

IJISA

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Abstract-In the field of database administration query optimization is one of the refinement processes. In recent years, huge volumes of data are flooded from different resources, which make query optimization, a difficult task for the researchers. In the crowd sourcing, environment query optimization is the biggest problem. The client is simply required to post an SQL-like subject, and the framework assumes the main issue of organizing the inquiry; execution setup is generated and in the crowd sourcing market places the evaluation plan evaluated. In order to retrieve data fast and reduce query processing time, Query optimization is badly required. In order to optimize the queries, Meta heuristic techniques are used. In this proposed paper, preprocessing method is used to mine the information from the Crowd. The Original population based ABC algorithm has low convergence speed. In this paper a novel A-BAT algorithm is proposed, which highly improve convergence speed, accuracy and Latency. This algorithm uses a Random walk phase. The proposed algorithm had better optimization accuracy, convergence rate, and robustness.

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“…In a recent decade, Swarm Intelligence (SI) algorithms have shown considerable success in solving many constraints optimization problems,LLS and attracted more attention in recent years. For examples of these algorithms [8]: Genetic Algorithm (GA) [9], Particle Swarm Optimization (PSO) [10],differential evolution(DE) [11], and Artificial Bee Colony (ABC) algorithm [12] and so on.…”

Section: Wherementioning

confidence: 99%

An Improved Artificial Bee Colony Algorithm for Linear Least Squares and Constrained Optimization Problems

Taher¹

2017

IJCA

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This paper proposes An Improved Artificial Bee Colony (Deb's-ABC) algorithm for solving constrained optimization problems and Nonnegative linear least squares problems. The proposed approach introduces different methods Based upon new search mechanism to balance exploration and exploitation abilities, generating initial population by using the orthogonal initialization method for achieving initial population that spread regularly over the feasible solution and to enhance the global convergence. In addition, we relax the Deb's rules by replacing the feasible solutions with the approximate feasible solutions in Deb's rules because some infeasible solutions with better objective function value and small violation may carry more important information than some feasible solutions .This algorithm is tested on several benchmark functions. Experimental results compared with a standard ABC and other algorithms show that the proposed algorithm is efficient and competitive algorithm for solving constrained optimization problems.

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Constrained artificial bee colony algorithm for optimization problems

Cited by 9 publications

References 16 publications

Research on Dynamic Facility Layout Problem of Manufacturing Unit Considering Human Factors

Research on Dynamic Facility Layout Problem of Manufacturing Unit Considering Human Factors

A Method of A-BAT Algorithm Based Query Optimization for Crowd Sourcing System

An Improved Artificial Bee Colony Algorithm for Linear Least Squares and Constrained Optimization Problems

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