Crossover-based artificial bee colony algorithm for constrained optimization problems

Brajević, Ivona

doi:10.1007/s00521-015-1826-y

Cited by 68 publications

(28 citation statements)

References 61 publications

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“…Recent improved variant of the ABC for COPs, called crossover-based artificial bee colony, is also used to solve the constrained Weber problem [54]. The main modifications introduced in the CB-ABC are related to the search operators used in each bee phase in order to improve the distribution of good information between solutions [54].…”

Section: Crossover-based Artificial Bee Colony Algorithm Formentioning

confidence: 99%

“…Hence 0 = 1.5 was adapted. For the ABC and CB-ABC algorithms, the values of the specific control parameters were taken from [53,54], where these algorithms were proposed to solve COPs. Especially for the CB-ABC, it was empirically determined that a lower value of the scout production period SPP is more appropriate for solving the CWP.…”

Section: Parameter Settingsmentioning

confidence: 99%

“…In this paper, four swarm-intelligence techniques are applied to solve this version of the constrained Weber problem: the artificial bee colony for constrained optimization [53], the crossover-based artificial bee colony (CB-ABC) algorithm [54], the firefly algorithm for constrained optimization [37], and the enhanced firefly algorithm (E-FA) [55]. The CB-ABC and the E-FA are two of the most recently proposed improved variants of the ABC and FA for solving constrained problems, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Application of Heuristic and Metaheuristic Algorithms in Solving Constrained Weber Problem with Feasible Region Bounded by Arcs

Stojanović

Brajević

Stanimirović

et al. 2017

Mathematical Problems in Engineering

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The continuous planar facility location problem with the connected region of feasible solutions bounded by arcs is a particular case of the constrained Weber problem. This problem is a continuous optimization problem which has a nonconvex feasible set of constraints. This paper suggests appropriate modifications of four metaheuristic algorithms which are defined with the aim of solving this type of nonconvex optimization problems. Also, a comparison of these algorithms to each other as well as to the heuristic algorithm is presented. The artificial bee colony algorithm, firefly algorithm, and their recently proposed improved versions for constrained optimization are appropriately modified and applied to the case study. The heuristic algorithm based on modified Weiszfeld procedure is also implemented for the purpose of comparison with the metaheuristic approaches. Obtained numerical results show that metaheuristic algorithms can be successfully applied to solve the instances of this problem of up to 500 constraints. Among these four algorithms, the improved version of artificial bee algorithm is the most efficient with respect to the quality of the solution, robustness, and the computational efficiency.

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Section: Crossover-based Artificial Bee Colony Algorithm Formentioning

confidence: 99%

Section: Parameter Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of Heuristic and Metaheuristic Algorithms in Solving Constrained Weber Problem with Feasible Region Bounded by Arcs

Stojanović

Brajević

Stanimirović

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Several population-based optimization algorithms have recently been proposed for solving the optimization problem in recent years, including genetic algorithms (GAs) (Lu et al 2014;Karthikeyan and Baskar 2015), particle swarm optimization (Kennedy and Eberhart 1995;Rezaee Jordehi 2014c;Rezaee Jordehi et al 2015), ant colony optimization (ACO) (Dorigo and Gambardella 1997;Terzi and Serin 2014;Viswanathan and Krishnamurthi 2015), bacterial foraging optimization (BFO) (Passino 2002;Panda et al 2014), Artificial Bee Colony (ABC) Yan et al 2015;Brajevic 2015), teaching-learning-based optimization (Crepinšek et al 2012;Rao and Waghmare 2014;Cheng 2014) and big bang-big crunch algorithm (Rezaee Jordehi 2014b). These population-based optimization algorithms have one or more populations consisting of a certain number of individuals, which present a solution of the problem to be solved, respectively.…”

Section: Introductionmentioning

confidence: 99%

A novel multi-population coevolution immune optimization algorithm

Xiao

Liu

et al. 2015

Soft Comput

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A novel multi-population coevolution immune optimization algorithm (MCIA) is proposed to solve numerical and engineering optimization problem in real world. MCIA is inspired by the mechanism that how neuroendocrine system affects T cells and B cells in immune system to eliminate the danger and the main idea of MCIA is to promote three populations, population B, population T and assistant population A, to coevolution through self-adjusted clone operator, the applied dislocation arithmetic crossover, cloud self-adapting mutation operator and local search operator to produce lymphocyte with high affinity. Self-adjusted clone operator and selecting elite elements in the memory population enable the search space be broadened and compressed, cloud self-adapting mutation operator characterized with randomness, stable topotaxis and local search technique enable global and local search be integrated to find the global optima with high population diversity. Therefore, several operators enable MCIA enjoy the capability of broadening the elite search space, boosting the global and local search around elites in search space. The performance comparisons of MCIA with three known immune algorithms and other three optimization algorithms in optimizing twelve benchmark functions indicate that MCIA is an effective algorithm for solving global optimization problems with high precision, good robustness and low time complexity.

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“…Togan et al [23] presented a design procedure employing the TLBO to the discrete optimization of planar steel frames. Yu et al [24] applied TLBO on several numerical and engineering optimization problems and proved that TLBO is more powerful than the improved bee algorithm (IBA) [25], the hybrid particle swarm optimization with differential evolution (PSO-DE) [26], the modified differential evolution algorithm (COMDE) [27], the g-best guided artificial bee colony (GABC) [28] and the upgraded artificial bee colony (UABC) [29] algorithms.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection Based on Multi-Scale Local Binary Patterns Operator and Improved Teaching-Learning-Based Optimization Algorithm

Zhang

Yang

2015

Symmetry

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Aiming to effectively recognize train center plate bolt loss faults, this paper presents an improved fault detection method. A multi-scale local binary pattern operator containing the local texture information of different radii is designed to extract more efficient discrimination information. An improved teaching-learning-based optimization algorithm is established to optimize the classification results in the decision level. Two new phases including the worst recombination phase and the cuckoo search phase are incorporated to improve the diversity of the population and enhance the exploration. In the worst recombination phase, the worst solution is updated by a crossover recombination operation to prevent the premature convergence. The cuckoo search phase is adopted to escape the local optima. Experimental results indicate that the recognition accuracy is up to 98.9% which strongly demonstrates the effectiveness and reliability of the proposed detection method.

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Crossover-based artificial bee colony algorithm for constrained optimization problems

Cited by 68 publications

References 61 publications

Application of Heuristic and Metaheuristic Algorithms in Solving Constrained Weber Problem with Feasible Region Bounded by Arcs

Application of Heuristic and Metaheuristic Algorithms in Solving Constrained Weber Problem with Feasible Region Bounded by Arcs

A novel multi-population coevolution immune optimization algorithm

Fault Detection Based on Multi-Scale Local Binary Patterns Operator and Improved Teaching-Learning-Based Optimization Algorithm

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