Tournament selection based antlion optimization algorithm for solving quadratic assignment problem

Kilic, Haydar; Yüzgeç, Uğur

doi:10.1016/j.jestch.2018.11.013

Cited by 38 publications

(24 citation statements)

References 29 publications

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“…Other improved versions of ALO have tried to develop new strategies with intent to overcome such shortcomings. We agree with the authors in [37] that the roulette wheel selection technique was not appropriate for ALO to choose the most effective solutions. In addition, we also adopt comments of authors in [36] that new solution generation function of ALO needs to be improved more effectively.…”

Section: Improved Antlion Optimization Algorithm (Ialo)supporting

confidence: 80%

“…However, the method has not been compared to other methods, especially state-of-the-art methods in recent years. Another modified ALO based on Tournament selection technique (TSALO) has been proposed and applied for optimizing quadratic assignment problem [37]. The authors have stated ALO has suffered from key drawbacks that stop ALO from finding very high quality solutions such as a long run time, local optimal stagnation and premature convergence to local optimum or near global optimum.…”

Section: Review On Previous Improved Antlion Optimization Algorithmmentioning

confidence: 99%

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Finding Solutions for Optimal Reactive Power Dispatch Problem by a Novel Improved Antlion Optimization Algorithm

Cao

Đại

et al. 2019

Energies

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In this paper, a novel improved Antlion optimization algorithm (IALO) has been proposed for solving three different IEEE power systems of optimal reactive power dispatch (ORPD) problem. Such three power systems with a set of constraints in transmission power networks such as voltage limitation of all buses, limitations of tap of all transformers, maximum power transmission limitation of all conductors and limitations of all capacitor banks have given a big challenge for global optimal solution search ability of the proposed method. The proposed IALO method has been developed by modifying new solution generation technique of standard antlion optimization algorithm (ALO). By optimizing three single objective functions of systems with 30, 57 and 118 buses, the proposed method has been demonstrated to be more effective than ALO in terms of the most optimal solution search ability, solution search speed and search stabilization. In addition, the proposed method has also been compared to other existing methods and it has obtained better results than approximately all compared ones. Consequently, the proposed IALO method is deserving of a potential optimization tool for solving ORPD problem and other optimization problems in power system optimization fields.

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Section: Improved Antlion Optimization Algorithm (Ialo)supporting

confidence: 80%

Section: Review On Previous Improved Antlion Optimization Algorithmmentioning

confidence: 99%

Finding Solutions for Optimal Reactive Power Dispatch Problem by a Novel Improved Antlion Optimization Algorithm

Cao

Đại

et al. 2019

Energies

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“…S [18,20,22], [4,5,6] 923.61 RL [17,20,20], [4,5,5] 841.30 RR [20,20,23], [5,5,6] 1,001.34 SL [18,20,21], [4,5,5] 861.88 SR [19,20,22], [5,5,6] 976.19…”

Section: %mentioning

confidence: 99%

“…Tournament Selection (TOS) and Roulette Wheel Selection (RWS) are general ways for making a selection. It was suggested that TOS is more effective than RWS [21][22][23]; the consequence is that a reasonable answer can be reached faster.…”

Section: Introductionmentioning

confidence: 99%

“…Kritwattanakorn, et al [27] used the RWS method for the selection approach in GA and suggested not to apply mutation for machine layout problems. However, based on the suggestions given by some research [21][22][23], the TOS method seems to be superior in finding a solution qualitatively (for instance, minimizing the total material handling cost) and obtain answers faster and closer to the optimum. In our paper presented initially to a conference [28], we related to the research of Kritwattanakorn, et al [27] that not to apply mutation, used TOS for selection, and showed how to deal with the vagueness of information and assist solving problems taken from earlier research works [25,26].…”

Section: Introductionmentioning

confidence: 99%

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Effects of Using Fuzzy Material Handling Inputs in the Genetic Algorithm for Machine Layout

Boongsood¹,

Jadram²

2019

Adv. sci. technol. eng. syst. j.

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This study introduces the implementation of fuzzy set theory to solve machine layout design issues, in order to handle vague information, using a genetic algorithm with tournament selection as the selection operator. The material handling inputs, including frequency and volume of materials that move between machines, were the parameters regarded as fuzzy numbers. The experimental results came from 2 case studies in a manufacturing system. In the first case, examining the difference in shapes of the triangular membership functions of input data, the total distances were reduced from 38.45 m to 29.72 m, a 22.71% reduction in distance. In the second case, examining the uncertainty of fuzzy data by an expert, the total distances were reduced from 103.45 m to 82.45 m, a 20.03% reduction in distance. It was found that given the uncertainty in input data, a shorter total material handling distance might not give a lower cost. The selection operator of tournament selection can compete effectively to converge to near the optimum solution. This can, therefore, be an alternative technique in managing manufacture.

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Implementation and comparative analysis of evolutionary algorithms for energy optimization in wireless sensor networks

Kundaliya

Hadia

2021

Int J Communication

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Summary Wireless sensor networks will be at the epitome of applications in near future. It is going through tremendous positive changes. Although it suffers from some limitations and it sorts out its limitations day by day. The biggest limitation of wireless sensor networks is the limited energy of the nodes. Most of the energy is used during the routing of the data. An optimized way of routing will save the valuable energy of the node and helpful for increasing the network's lifetime. The optimization algorithms are indeed utterly helpful in many applications across the different fields to optimize the resources. In the proposed worked we have applied Adaptive Particle Swarm Optimization (APSO), Ant Colony Optimization (ACO), Genetic Algorithms (GA), and Simulated Annealing (SA) to a Modified Rendezvous Point Selection Scheme and observed the effect on the network lifetime and energy consumption in the network. We have made an exhaustive comparison of all the optimization algorithms with considerable simulations.

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Tournament selection based antlion optimization algorithm for solving quadratic assignment problem

Cited by 38 publications

References 29 publications

Finding Solutions for Optimal Reactive Power Dispatch Problem by a Novel Improved Antlion Optimization Algorithm

Finding Solutions for Optimal Reactive Power Dispatch Problem by a Novel Improved Antlion Optimization Algorithm

Effects of Using Fuzzy Material Handling Inputs in the Genetic Algorithm for Machine Layout

Implementation and comparative analysis of evolutionary algorithms for energy optimization in wireless sensor networks

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