Solving shortest path problem using particle swarm optimization

Mohemmed, Ammar; Sahoo, Niranjan; Geok, Tan Kim

doi:10.1016/j.asoc.2008.01.002

Cited by 157 publications

(97 citation statements)

References 9 publications

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“…It is required to propose more competent approaches to understand SPP. With regard to difficulty of SPP; metaheuristics can also determine superior routes in a proper time (Mohemmed et al, 2008). Pervious researches also support the benefits of neural networks (NN) in handling SPP (Ahn et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

“…Their paper revealed that GA outperforms NN-based methods in resolving SPP. In 2008, PSO was tested to solve SPP and the achieved results demonstrated that the PSO-based routes are better than those of GA (Mohemmed et al, 2008). In 2010, an ant colony technique (ACO) has been proposed to assess SPP (Ghoseiri and Nadjari, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…It has been proved that the bi-criterion version of crisp SPP can be treated as a NP-hard problem. Hence, metaheuristic approaches are also useful to tackle SPP, especially for largescale and real-time requests (Mohemmed et al, 2008). Trevizan and Veloso (2014) introduced depth-based SPP techniques (Trevizan and Veloso, 2014).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Modified Genetic Algorithm for Finding Fuzzy Shortest Paths in Uncertain Networks

Heidari¹,

Delavar²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In realistic network analysis, there are several uncertainties in the measurements and computation of the arcs and vertices. These uncertainties should also be considered in realizing the shortest path problem (SPP) due to the inherent fuzziness in the body of expert's knowledge. In this paper, we investigated the SPP under uncertainty to evaluate our modified genetic strategy. We improved the performance of genetic algorithm (GA) to investigate a class of shortest path problems on networks with vague arc weights. The solutions of the uncertain SPP with considering fuzzy path lengths are examined and compared in detail. As a robust metaheuristic, GA algorithm is modified and evaluated to tackle the fuzzy SPP (FSPP) with uncertain arcs. For this purpose, first, a dynamic operation is implemented to enrich the exploration/exploitation patterns of the conventional procedure and mitigate the premature convergence of GA technique. Then, the modified GA (MGA) strategy is used to resolve the FSPP. The attained results of the proposed strategy are compared to those of GA with regard to the cost, quality of paths and CPU times. Numerical insta nces are provided to demonstrate the success of the proposed MGA-FSPP strategy in comparison with GA. The simulations affirm that not only the proposed technique can outperform GA, but also the qualities of the paths are effectively improved. The results clarify that the competence of the proposed GA is preferred in view of quality quantities. The results also demonstrate that the proposed method can efficiently be utilized to handle FSPP in uncertain networks.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Modified Genetic Algorithm for Finding Fuzzy Shortest Paths in Uncertain Networks

Heidari¹,

Delavar²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…In regard to artificial life, PSO has ties with bird flocking and fish schooling theories, while in regard to evolutionary computation has similarities with genetic and evolutionary algorithms. Since its invention, PSO has been applied with success on various COPs such as, the unit commitment problem 31 , the traveling salesman problem 32 , the task assignment problem 33 , an optimal operational path finding for automated drilling operations 34 , a multi-objective order planning production problem in steel sheets manufacturing 35 , scheduling problems involving duedates 29 , the shortest path problem 36 , etc. Recently, its application has been extended on scheduling problems such as, flow-shop scheduling problems [37][38][39][40][41] , the singlemachine total weighting tardiness problem 27,42 , the single machine scheduling problem with periodic maintenance 28 , the two-stage assembly-scheduling problem 43 , and job-shop scheduling problems 44,45 .…”

Section: The Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

A Particle Swarm Optimization Algorithm for Scheduling Against Restrictive Common Due Dates

Nearchou¹,

Omirou²

2013

IJCIS

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Abstract:Focusing on the just-in-time (JIT) operations management, earliness as well as, tardiness of jobs' production and delivery should be discouraged. In accordance to this philosophy, scheduling problems involving earliness and tardiness penalties are very critical for the operations manager. In this paper, a new population heuristic based on the particle swarm optimization (PSO) technique is presented to solve the single machine early/tardy scheduling problem against a restrictive common due date. This type of scheduling sets costs depending on whether a job finished before (earliness), or after (tardiness) the specified due date. The objective is to minimize a summation of earliness and tardiness penalty costs, thus pushing the completion time of each job as close as possible to the due date. The problem is known to be NP-hard, and therefore large size instances cannot be addressed by traditional mathematical programming techniques. The performance of the proposed PSO heuristic is measured over benchmarks problems with up to 1000 jobs taken from the open literature, and found quite high and promising in respect to the quality of the solutions obtained. Particularly, PSO was found able to improve the 82% of the existing best known solutions of the examined benchmarks test problems.

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“…Mohemmed proposed a modified priority-based encoding incorporating a heuristic operator for reducing the possibility of loop-formation in the path construction process. It could find closer sub-optimal paths with high certainty for all the tested networks [9]. Vincent used the genetic algorithm (GA) and the particle swarm optimization algorithm (PSO) to cope with the complexity of the problem and compute feasible and quasi-optimal trajectories in a complex 3D environment by using a parallel implementation [10].…”

Section: Introductionmentioning

confidence: 99%

Research on Path Optimization with PSO for Unmanned Vehicle

Jia

Lei

2015

Int. J. Onl. Eng.

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Abstract-The development of autonomous unmanned vehicles is of high interest to many organizations around the world and path planning is the key point of the navigation for the autonomous unmanned vehicle. Intelligent algorithms have been applied in this field and an essential aspect of unmanned vehicles autonomy is the ability for automatic path planning. In this paper, particle swarm optimization algorithm as one of new swarm intelligent optimization methods is introduced into a path planning for autonomous vehicle, which is constructed of a particle representation methods for vehicle routing problem with fast convergence speed. The results show that the particle swarm optimization algorithm can obtain the solution of the vehicle routing problem quickly and effectively. It is a good method for solving the vehicle routing problem.

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Solving shortest path problem using particle swarm optimization

Cited by 157 publications

References 9 publications

A Modified Genetic Algorithm for Finding Fuzzy Shortest Paths in Uncertain Networks

A Modified Genetic Algorithm for Finding Fuzzy Shortest Paths in Uncertain Networks

A Particle Swarm Optimization Algorithm for Scheduling Against Restrictive Common Due Dates

Research on Path Optimization with PSO for Unmanned Vehicle

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