Application Of Chicken Swarm Optimization to Travelling Salesman Problem And A Reviewing Of Similar Studies

Yıl 2022, Sayı: 15, 65 - 72, 22.06.2022

Süleyman Özer , Adil Baykasoğlu , Özcan Kılınçcı

Öz

This study focuses on an adapted application of the Chicken Swarm Optimization (CSO) Algorithm on a Travelling Salesman Problem (TSP). CSO Algorithm aims to search for optimal solution of a continuous function metaheuristically as a basis and it need some modifications to be coupled to a discontinuous problem like TSP. Some studies have been done before in the process of transforming a continuous metaheuristic method into discontinuous. However, as seen in reference studies, the algorithm needs also an additional decision-making mechanism after the transformation, and this would usually be the Greedy Search (GS) Algorithm when it comes to the CSO. Nevertheless, the aftermath of these decision-making mechanisms the customized novel CSO leaves the main logic of CSO and being Swarm Intelligence Algorithm and turn into a more colorful variation of the casual GS algorithm. The original part that distinguishes this work from others, it is focused on applying the CSO algorithm to a discontinuous TSP problem, while staying true to neutral phenomenon mimicked method and preserve the CSO’s logical context. The main quest of the paper is not to invent a method that gives better results for the example problem on any account, but to reveal how the CSO algorithm will give results to the example problem if it maintains its logic integrity. Therefore, an extension free bare adaptation of CSO is implemented for a TSP problem and results are observed.

Anahtar Kelimeler

Chicken Swarm Optimization, CSO, TSP, Greedy Search, Metaheuristic, Discontinuous Transformation.

Kaynakça

• Meng, Xianbing, et al. A new bio-inspired algorithm: chicken swarm optimization. In: International conference in swarm intelligence. Springer, Cham, 2014. p. 86-94.
• Deb, Sanchari, et al. Recent studies on chicken swarm optimization algorithm: a review (2014–2018). Artificial Intelligence Review, 2020, 53.3: 1737-1765.
• Mohamed, Taha M. Enhancing the performance of the greedy algorithm using chicken swarm optimization: An application to exam scheduling problem. Egyptian Computer Science Journal, 2018, 42.1: 1-17.
• Hafez, Ahmed Ibrahem, et al. An innovative approach for feature selection based on chicken swarm optimization. In: 2015 7th international conference of soft computing and pattern recognition (SoCPaR). IEEE, 2015. p. 19-24.
• Huang, Ko-Wei, et al. A hybrid crow search algorithm for solving permutation flow shop scheduling problems. Applied Sciences, 2019, 9.7: 1353.
• Bean, James C. Genetic algorithms and random keys for sequencing and optimization. ORSA journal on computing, 1994, 6.2: 154-160.
• Han, Meng; liu, Sanyang. An improved binary chicken swarm optimization algorithm for solving 0-1 knapsack problem. In: 2017 13th International Conference on Computational Intelligence and Security (CIS). IEEE, 2017. p. 207-210.
• Liu, Yuanjie; Liu, Qiang; tang, Zhi. A discrete chicken swarm optimization for traveling salesman problem. In: Journal of Physics: Conference Series. IOP Publishing, 2021. p. 012034.
• Taie, Shereen A.; Ghonaim, Wafaa. CSO-based algorithm with support vector machine for brain tumor's disease diagnosis. In: 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, 2017. p. 183-187.
• Sutoyo, Edi, et al. Application of adaptive neuro-fuzzy inference system and chicken swarm optimization for classifying river water quality. In: 2017 5th International Conference on Electrical, Electronics and Information Engineering (ICEEIE). IEEE, 2017. p. 118-122.
• Muralikrishnan, N. Chicken swarm optimization for economic dispatch with disjoint prohibited zones considering network losses. Journal of Applied Science and Engineering Methodologies, 2016, 2.2: 255-259.
• Wang, Qingxi; zhu, Lihua. Optimization of wireless sensor networks based on chicken swarm optimization algorithm. In: AIP conference proceedings. AIP Publishing LLC, 2017. p. 020197.
• Banerjee, Subhabrata; chattopadhyay, Sudipta. Improved serially concatenated convolution turbo code (SCCTC) using chicken swarm optimization. In: 2015 IEEE Power, Communication and Information Technology Conference (PCITC). IEEE, 2015. p. 268-273.
• Li, Yongtao; wu, Yu; qu, Xiangju. Chicken swarm–based method for ascent trajectory optimization of hypersonic vehicles. Journal of Aerospace Engineering, 2017, 30.5: 04017043.
• Al Shayokh, Md; shin, Soo Young. Bio inspired distributed WSN localization based on chicken swarm optimization. Wireless Personal Communications, 2017, 97.4: 5691-5706.
• Ren, Wei, et al. Identification of fast-steering mirror based on chicken swarm optimization algorithm. In: IOP conference series: earth and environmental science. IOP Publishing, 2017. p. 012086.