Improved Chicken Swarm Optimization Algorithm to Solve the Travelling Salesman Problem

Chebihi, Fayçal; Riffi, Mohammed Essaid; Agharghor, Amine; Semlali, Soukaina Cherif Bourki; Haily, Abdelfattah

doi:10.11591/ijeecs.v12.i3.pp1054-1062

Cited by 9 publications

(4 citation statements)

References 21 publications

(30 reference statements)

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“…If we want to solve that we need (𝑛−1)! 2 [31] comparisons, which makes it impossible to be solved theoretically. We want to improve the time and the complexity of this problem by using the WOA algorithm discovered by Mirjalili, and a data mining technique called K-means.…”

Section: The Travelling Salesman Problem (Tsp)mentioning

confidence: 99%

Solving a traveling salesman problem using meta-heuristics

Nejad

Fazekas

2022

IJ-AI

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In this article, we have introduced an advanced new method of solving a traveling salesman problem (TSP) with the whale optimization algorithm (WOA), and K-means which is a partitioning-based algorithm used in clustering. The whale optimization algorithm first was introduced in 2016 and later used to solve a TSP problem. In the TSP problem, finding the best path, which is the path with the lowest value in the fitness function, has always been difficult and time-consuming. In our algorithm, we want to find the best tour by combining it with K-means which is a clustering method. In other words, we want to divide our problem into smaller parts called clusters, and then we join the clusters based on their distances. To do this, the WOA algorithm, TSP, and K-means must be combined. Separately, the WOA-TSP algorithm which is an unclustered algorithm is also implemented to be compared with the proposed algorithm. The results are shown through some figures and tables, which prove the effectiveness of this new method.

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Section: The Travelling Salesman Problem (Tsp)mentioning

confidence: 99%

Solving a traveling salesman problem using meta-heuristics

Nejad

Fazekas

2022

IJ-AI

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“…Similarly, formation at middle are considered as hens. The swarm is randomly differentiated into groups, each of which contains a rooster, a group of hens and chicks [23]. The rooster with the highest fitness value has the ability to look for food in more places and on a greater scale.…”

Section: Chicken Swarm Intelligence (Csi)mentioning

confidence: 99%

A Self-Stabilizing and Parameter-Optimized Deep Learning Model for Predicting Mental Health Disorder

2022

IJIES

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A mental health disorder (MHD) is a behavioural or mental pattern that causes significant distress or impairment of personal functioning. Daily distress faced in social, work, or family activities are associated with MHD which severely affects the person's life style leading to increase of death cases. So, it is very important to detect the MHD at earlier stage itself. Many machine learning (ML) techniques have been suggested to detect MHD based on patient data. But, the ML methods cannot process the large samples efficiently. So, the deep learning (DL) algorithms have been developed for the detection of MHD efficiently. But still, fixed network parameters used in DL methods cannot always perform well for large scale datasets. Also, irrelevant feature in the datasets degrades the performances of DL models. Moreover, stabilization will be another issue of the DL methods. To resolve the above mentioned problems, a chicken swarm intelligence improved self stabilized deep neural network (CSI-ISDNN) is proposed in this article to detect and diagnose the MHD more efficiently. The proposed method employs CSI on training dataset where CS will select the best optimal features and parameters for ISDNN. The fitness value of the CS is then calculated and analysed to find optimised prediction results of ISDNN for MHD with the goal of reducing prediction error. The stabilization of ISDNN is standardized by introducing some additional parameters along with original network parameters. The trained CSI-ISDNN model is used to predict test datasets. Finally, the experimental results exhibit that the CSI-ISDNN model achieves an accuracy of 97.4% on the OSMI mental health database. The accuracy of CSI-ISDNN is superior by 10.18%, 6.21%, 2.63%, and 0.0093% compared to classic model like IGCBA-BPNN, CNN-RNN, CNN-Bi-LSTM, and CSI-MLP respectively.

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“…A graph illustrates the cities' locations and distances. Many studies, such as [1], [5]- [7] are working to solve the TSP and reduce its complexity to obtain an ideal solution. This paper proposes a parallel method for solving TSP using BnB.…”

Section: Introductionmentioning

confidence: 99%

A parallel algorithm to find the exact solution of the travelling salesman problem

Al-Neama

Ahmed

Ali

2023

IJEECS

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The traveling salesman problem (TSP) is a problem in computer science that has been extensively studied and has a wide variety of real-world applications. It is considered an NP-hard issue since the only way to get a precise solution to it is to wait an exponentially long amount of time unless P=NP. Both accurate and heuristic algorithms exist to tackle this problem. The Branch-and-Bound algorithm (BnB) is often regarded as the most significant precise approach, despite the fact that it traverses, in the worst scenario, all potential tours in order to calculate the tour efficiently. This study proposed an efficient parallel algorithm to solve TSP by using a cluster multicore system. An exact algorithm will be used to get the optimal solution, and to expedite the search for the best path, we have employed a multi-threaded approach that capitalizes on the processing power of multiple CPU cores to concurrently process sub-problems.

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Improved Chicken Swarm Optimization Algorithm to Solve the Travelling Salesman Problem

Cited by 9 publications

References 21 publications

Solving a traveling salesman problem using meta-heuristics

Solving a traveling salesman problem using meta-heuristics

A Self-Stabilizing and Parameter-Optimized Deep Learning Model for Predicting Mental Health Disorder

A parallel algorithm to find the exact solution of the travelling salesman problem

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