Particle Swarm Optimization Algorithm for the Traveling Salesman Problem

Goldbarg, Elizabeth Ferreira Gouvêa; Goldbarg, Marco César; Souza, Givanaldo R. de

doi:10.5772/5580

Cited by 28 publications

(11 citation statements)

References 28 publications

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“…The aim is to know the performance of the CSO algorithm to solve this problem, the TSPLIB is used, the goal is to approached to the best-known optimum of selected instances found by other methods like simulated annealing [12][13], Tabu search [14][15], Genetic algorithm [16][17][18][19], Ant colony optimization [20][21], Particle swarm optimization [22][23][24][25], Bee colony optimization [26][27].…”

Section: B Resultsmentioning

confidence: 99%

Discrete cat swarm optimization algorithm applied to combinatorial optimization problems

Bouzidi

Riffi

2014

2014 5th Workshop on Codes, Cryptography and Communication Systems (WCCCS)

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the traveling salesman problem (TSP), and the quadratic assignment problem (QAP) are two combinatorial optimization problems with a diverse set of applications. This research paper aims to apply the adaptation of discrete cat swarm optimization algorithm inspired by the natural behavior of cats to solve TSP and QAP. Simulated experiments were conducted on several benchmark instances taken from the OR-library. The results show the effectiveness of the proposed adaptation to solve real applications area based on the two-studied problems.

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Section: B Resultsmentioning

confidence: 99%

Discrete cat swarm optimization algorithm applied to combinatorial optimization problems

Bouzidi

Riffi

2014

2014 5th Workshop on Codes, Cryptography and Communication Systems (WCCCS)

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“…The function rand(0, 1) refers to randomly generated numbers, and P t id represents the current best position of the i th particle at t iteration, and P t gd represents the global best particle of the entire population. Interested readers are referred to [82] for the basic PSO algorithm, while the pseudocode for the modified PSO clustering algorithm is shown in Algorithm listing 3.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Nature-inspired metaheuristic techniques for automatic clustering: a survey and performance study

Ezugwu

2020

SN Appl. Sci.

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The application of several swarm intelligence and evolutionary metaheuristic algorithms in data clustering problems has in the past few decades gained wide popularity and acceptance due to their success in solving and finding good quality solutions to a variety of complex real-world optimization problems. Clustering is considered one of the most important data analysis techniques in the domain of data mining. A clustering problem refers to the partitioning of unlabeled data objects into a certain number of clusters based on their attribute values or features, with the objective of maximizing intra-clusters homogeneity and inter-cluster heterogeneity. This paper presents an up-to-date survey of major nature-inspired metaheuristic algorithms that have been employed to solve automatic clustering problems. Further, a comparative study of several modified well-known global metaheuristic algorithms is carried out to solve automatic clustering problems. Also, three hybrid swarm intelligence and evolutionary algorithms, namely, particle swarm differential evolution algorithm, firefly differential evolution algorithm and invasive weed optimization differential evolution algorithm, are proposed to deal with the task of automatic data clustering. In contrast to many of the existing traditional and evolutionary computational clustering techniques, the clustering algorithms presented in this paper do not require any predetermined information or prior-knowledge of the dataset that is to be classified, but rather they are capable of spontaneously identifying the optimal number of partitions of the data points during the course of program execution. Forty-one benchmarked datasets that comprise eleven artificial and thirty real world datasets are collated and utilized to evaluate the performances of the representative nature-inspired clustering algorithms. According to the extensive experimental results, comparisons and statistical significance, the firefly algorithm appeared to be more appropriate for better clustering of both low and high dimensional data objects than were other state-of-the-art algorithms. Further, an experimental study demonstrates the superiority of the three proposed hybrid algorithms over the standard stateof-the-art methods in finding meaningful clustering solutions to the problem at hand.

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“…Evolution of transition path using MPSO. The search for the lowest-energy transition path is similar to the traveling salesman problem (TSP) 40 . Instead of finding the shortest path between cities, the objective here is to locate the lowest energy path connecting the initial and final structures.…”

Section: Generation Of a Suitable Simulation Cellmentioning

confidence: 99%

An automated predictor for identifying transition states in solids

et al. 2020

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The minimum energy path (MEP) and transition state are two key parameters in the investigation of the mechanisms of chemical reactions and structural phase transformations. However, determination of transition paths in solids is challenging. Here, we present an evolutionary method to search for the lowest energy path and the transition state for pressure-induced structural transformations in solids without any user input or prior knowledge of possible paths. Instead, the initial paths are chosen stochastically by connecting randomly selected atoms from the initial to final structure. The MEP of these trials paths were computed and ranked in order of their energies. The matrix particle swarm optimization algorithm is then used to generate improved transition paths. The procedure is repeated until the lowest energy MEP is found. This method is validated by reproducing results of several known systems. The new method also successfully located the MEP for the direct low-temperature pressure induced transformation of face centered-cubic (FCC) silicon to the simple hexagonal(sh) phase and FCC lithium to a complex body centered-cubic cI16 high-pressure phase. The proposed method provides a convenient, robust, and reliable approach to identify the MEP of phase transformations. The method is general and applicable to a variety of problems requiring the location of the transition state.

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Particle Swarm Optimization Algorithm for the Traveling Salesman Problem

Cited by 28 publications

References 28 publications

Discrete cat swarm optimization algorithm applied to combinatorial optimization problems

Discrete cat swarm optimization algorithm applied to combinatorial optimization problems

Nature-inspired metaheuristic techniques for automatic clustering: a survey and performance study

An automated predictor for identifying transition states in solids

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