A Comparative Performance Analysis of Computational Intelligence Techniques to Solve the Asymmetric Travelling Salesman Problem

Odili, Julius Beneoluchi; Ahmad, Nesar; Zarina, M.

doi:10.1155/2021/6625438

Cited by 7 publications

(9 citation statements)

References 61 publications

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“…The evaluation metrics used in the comparative performance analysis in this study are the algorithms efficiency and effectiveness. Effectiveness as used in this study refers to the capacity of the algorithms to algorithms to obtain the optimal results while algorithm efficiency refers to the algorithms capacity to obtain results using the most optimized resources 44 , 45 .…”

Section: Implementation Of the Abo And The Csmentioning

confidence: 99%

“…For emphasis, note that algorithm effectiveness is the capacity of algorithms to arrive at the global optimum but efficiency refers to the algorithm’s capacity to minimize the use of computer resources. Since the amount of time spent to arrive at a solution correlates with use of computer resources, the amount of time taken to arrive at a solution dictates the length of time that the computer resources are engaged: the shorter the time, the better 44 .…”

Section: Comparative Performance Of Abo and Flower Pollination Algorithmmentioning

confidence: 99%

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Stochastic process and tutorial of the African buffalo optimization

Odili¹,

Ahmad

Alkazemi

et al. 2022

Sci Rep

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This paper presents the data description of the African buffalo optimization algorithm (ABO). ABO is a recently-designed optimization algorithm that is inspired by the migrant behaviour of African buffalos in the vast African landscape. Organizing their large herds that could be over a thousand buffalos using just two principal sounds, the /maaa/ and the /waaa/ calls present a good foundation for the development of an optimization algorithm. Since elaborate descriptions of the manual workings of optimization algorithms are rare in literature, this paper aims at solving this problem, hence it is our main contribution. It is our belief that elaborate manual description of the workings of optimization algorithms make it user-friendly and encourage reproducibility of the experimental procedures performed using this algorithm. Again, our ability to describe the algorithm’s basic flow, stochastic and data generation processes in a language so simple that any non-expert can appreciate and use as well as the practical implementation of the popular benchmark Rosenbrock and Shekel Foxhole functions with the novel algorithm will assist the research community in benefiting maximally from the contributions of this novel algorithm. Finally, benchmarking the good experimental output of the ABO with those of the popular, highly effective and efficient Cuckoo Search and Flower Pollination Algorithm underscores the ABO as a worthy contribution to the existing body of population-based optimization algorithms

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Section: Implementation Of the Abo And The Csmentioning

confidence: 99%

Section: Comparative Performance Of Abo and Flower Pollination Algorithmmentioning

confidence: 99%

Stochastic process and tutorial of the African buffalo optimization

Odili¹,

Ahmad

Alkazemi

et al. 2022

Sci Rep

Self Cite

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“…At this level, the vultures of the different groups explore to best global optima (or food) in the search domain and all this procedure is evaluated by the following modified Equation (48). From afterwards, the hunting behavior of the vultures of the entire groups has been evaluated by the following modified Equations ( 49) and ( 50):…”

Section: Canonical Avoa Phasementioning

confidence: 99%

“…To attain demanding and unbiased contrast, descriptive numerical and statistical trials have been utilized. Similarly, many optimization techniques have been utilized for studying the best outcomes to the asymmetric TSP by Odili et al 48 On the basis of the trial, simulations are shown that the asymmetric travelling salesman problem (ATSP) is able to attain the best outcomes for all test instances than others.…”

Section: Introductionmentioning

confidence: 99%

An efficient improved African vultures optimization algorithm with dimension learning hunting for traveling salesman and large‐scale optimization applications

Singh

Houssein

Mirjalili

et al. 2022

Int J of Intelligent Sys

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Exploring the finest shortest‐path traveling salesman optimization application is a typical NP‐hard problem. Similarly the solution of the large‐scale optimization applications is also a big challenging issue in front of scientists. First, African Vultures Optimization Algorithm (AVOA) was developed to resolve continuous applications where it performed fine. In the last few months, many enhanced strategies of AVOA have been offered in recent literature works and it has been extensively utilized to resolve large‐scale engineering optimization applications. This study offers a newly modified dimension learning hunting (DLH)‐based AVOA called DLHAV algorithm to resolve highly complex continuous and discrete applications. It helps improve the imbalance amid the hunting (or exploitation) and search (or exploration), the lack of crowd diversity, slow convergence speed, trapping in local optima, and early convergence of the AVOA variant. The proposed strategy benefits from a newly driven approach called the DLH search approach congenital from the separate exploitation behavior of vultures in the search domain. DLH exploration strategy utilizes a distinct method to make the best neighborhood for all vultures in which the nearest member information can be supplied amid vultures. DLH helps in improving the balance amid global and local and sustains diversity. To scrutinize the performance of DLHAV, the solutions of the DLHAV method are verified on 29‐CEC'17 and 10‐CEC'20 with familiar comparative methods and some other classical optimization approaches over many familiar traveling salesman problem/large‐scale instances. With the intention of attaining unbiased and rigorous comparison, descriptive statistics such as standard deviation and mean have been applied, and the statistical Friedman test is also conducted. The experimental solution carried out in this study has revealed that the proposed algorithm outperforms significantly over the other alternative optimizers.

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“…Rashid and Mosteiro have provided a novel solution in [21] that integrates local-search heuristics, a greedy algorithm and a genetic algorithm. Odili et al in [22] present a comparative performance analysis of some of the metaheuristic algorithms like the improved extremal optimization (IEO), african buffalo optimization algorithm (ABO), max-min ant system (MMAS), the heuristic randomized insertion algorithm (RAI) and cooperative genetic ant system (CGAS) to solve the ATSP. Fosin et al have presented a new parallel iterated local search approach in [23] with 2-opt and 3-opt operators for symmetric TSP, using GPU acceleration.…”

Section: Introductionmentioning

confidence: 99%

Parallelized solution to the asymmetric travelling salesman problem using central processing unit acceleration

Arya

Perumal

Krishnan

2022

IJEECS

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<p><span>Travelling salesman problem is a well researched problem in computer science and has many practical applications. It is classified as a NP-hard problem as its exact solution can only be obtained in exponential time unless P = NP. There are different variants of the travelling salesman problem (TSP) and in this paper, asymmetric travelling salesman problem is addressed since this variant is quite often observed in real world scenarios. There are a number of heuristic approaches to this problem which provides approximate solutions in polynomial time, however this paper proposes an exact optimal solution which is accelerated with the help of multi-threading-based parallelization. In order to find the exact optimal solution, we have used the held-karp algorithm involving dynamic programming and to reduce the time taken to find the optimal path, we have used a multi-threaded approach to parallelize the processing of sub-problems by leveraging the central processing unit cores (CPUs). This method is an extension of a well researched solution to the TSP; however, this method shows that solutions to computationally intensive problems involving sub-problems such as the asymmetic travelling salesman problem (ATSP) can be accelerated with the help of modern CPUs.</span></p>

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A Comparative Performance Analysis of Computational Intelligence Techniques to Solve the Asymmetric Travelling Salesman Problem

Cited by 7 publications

References 61 publications

Stochastic process and tutorial of the African buffalo optimization

Stochastic process and tutorial of the African buffalo optimization

An efficient improved African vultures optimization algorithm with dimension learning hunting for traveling salesman and large‐scale optimization applications

Parallelized solution to the asymmetric travelling salesman problem using central processing unit acceleration

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