Coordinating Particle Swarm Optimization, Ant Colony Optimization and K-Opt Algorithm for Traveling Salesman Problem

Khan, Indadul; Maiti, Manas Kumar; Maiti, Manoranjan

doi:10.1007/978-981-10-4642-1_10

Cited by 16 publications

(6 citation statements)

References 33 publications

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“…25 These sequences are then refined with a k-opt algorithm that exploits transfer cost graph symmetries, and are perturbed by the incorporation of time-dependent observable windows and off-angle stationkeeping costs. 26 A high-level overview is shown in Fig. 5.…”

Section: Hybrid Metaheuristic Methodsmentioning

confidence: 99%

Exoplanet imaging scheduling optimization for an orbiting starshade working with Extremely Large Telescopes

Peretz

Mather

Hall

et al. 2021

J. Astron. Telesc. Instrum. Syst.

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We present optimized observation schedules for a distributed configuration of the Remote Occulter Mission. Accounting for refueling rounds, we show that an Earth-orbiting Remote Occulter could enable up to 158 ground-based observations of 80 exoplanetary targets in a mission lifetime. We develop two target lists, provide exposure time estimates for each potential target star, present an analytic approach for determining target observability, and estimate the cost of station-keeping and retargeting maneuvers required to maintain such a mission. We optimize the mission observation schedule over these cost and science delivery estimates using deterministic and metaheuristic optimization methods with varying degrees of operator intervention and conclude by assessing mission profile sensitivity to both isolated and accumulated cost and design perturbations. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Hybrid Metaheuristic Methodsmentioning

confidence: 99%

Exoplanet imaging scheduling optimization for an orbiting starshade working with Extremely Large Telescopes

Peretz

Mather

Hall

et al. 2021

J. Astron. Telesc. Instrum. Syst.

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“…In [18], [19], [20], some methods are proposed for improving the swap sequence based PSO and they are applied to TSP.…”

Section: Related Workmentioning

confidence: 99%

“…In [20], a hybrid algorithm based on the swap sequence based PSO, ACO and K-opt operation is proposed for solving TSP. In the proposed algorithm, ACO is first used to generate an initial set of solutions which is then used to initialize the swarm for PSO.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Swap Sequence-Based Particle Swarm Optimization Algorithm to Solve TSP

et al. 2021

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The Traveling Salesman Problem (TSP) is a combinatorial optimization problem that is useful in a number of applications. Since there is no known polynomial-time algorithm for solving large scale TSP, metaheuristic algorithms such as Ant Colony Optimization (ACO), Bee Colony Optimization (BCO), and Particle Swarm Optimization (PSO) have been widely used to solve TSP problems through their high quality solutions. Several variants of PSO have been proposed for solving discrete optimization problems like TSP. Among them, the basic algorithm is the swap sequence based PSO, however, it does not perform well in providing high quality solutions. To improve the performance of the swap sequence based PSO, this paper introduces an enhanced swap sequence based PSO algorithm by integrating the strategies of the Expanded PSO (XPSO) in the swap sequence based PSO. This is because although XPSO is only suitable for solving continuous optimization problems, it has a high performance among the variants of PSO. In our work, the TSP problem is used to model a package delivery system in the Kuala Lumpur area. The problem set consists of 50 locations in Kuala Lumpur. Our aim is to find the shortest route in the delivery system by using the enhanced swap sequence based PSO. We evaluate the algorithm in terms of effectiveness and efficiency while solving TSP. To evaluate the proposed algorithm, the solutions to the TSP problem obtained from the proposed algorithm and swap sequence based PSO are compared in terms of the best solution, mean solution, and time taken to converge to the optimal solution. The proposed algorithm is found to provide better solutions with shorter paths when applied to TSP as compared to swap sequence based PSO. However, the swap sequence based PSO is found to converge faster than the proposed algorithm when applied to TSP.

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“…To test the time performance of CSP-MCTS, we compare the time between CSP-MCTS algorithm and PSO + ACO + kOPT [23] algorithm with less time complexity. The relevant experiment results are shown in Figure 6, the ordinate represents the average running time of 50 times, the unit is second.…”

Section: B Performance Test Experimentsmentioning

confidence: 99%

A Path Planning Method Based on Improved Single Player-Monte Carlo Tree Search

2020

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With the thorough application and development of Intelligent Logistics System, the Path Planning Problem, which is one of the key problems of logistics scheduling, is accelerating to the direction of dynamics, diversification and complexity. In order to better adapt to the new changes, this paper proposes a path planning method based on improved SP-MCTS (Single-Player Monte Carlo Tree Search) algorithm. Unlike the previous heuristic algorithms, SP-MCTS does not completely depend on parameters, and can more effectively solve variable combinatorial optimization problems. Aiming at the problem of path planning, this paper enhances the simulation process of SP-MCTS by clonal selection algorithm, in order to improve the algorithm's accuracy of locating high-potential branches, and further improve the accuracy of the decisions made by SP-MCTS. Experiments show that the method proposed in this paper is less affected by the parameters and can better solve the changing path planning problem. Compared with the existing path planning methods and original SP-MCTS algorithm, it can solve the changeable path planning problem more stably, faster and more accurately. INDEX TERMS single player-monte carlo tree search; clonal selection algorithm; path planning

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Coordinating Particle Swarm Optimization, Ant Colony Optimization and K-Opt Algorithm for Traveling Salesman Problem

Cited by 16 publications

References 33 publications

Exoplanet imaging scheduling optimization for an orbiting starshade working with Extremely Large Telescopes

Exoplanet imaging scheduling optimization for an orbiting starshade working with Extremely Large Telescopes

An Enhanced Swap Sequence-Based Particle Swarm Optimization Algorithm to Solve TSP

A Path Planning Method Based on Improved Single Player-Monte Carlo Tree Search

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