Finding Multi-Objective Shortest Paths Using Memory-Efficient Stochastic Evolution Based Algorithm

Siddiqi, Umair F.; Shiraishi, Yasuyuki; Dahb, Mona Abo El; Sait, Sadiq M.

doi:10.1109/icnc.2012.35

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…The majority of existing acceleration techniques for multicriteria shortest path search are heuristics returning nearly optimal solutions. Some approaches are based on heuristic optimization methods, for example stochastic evolutionary algorithms [14,15]. Other ones employ standard label-setting or label-correcting techniques combined with relaxation heuristics providing substantial speedups at the cost of solution optimality.…”

Section: Related Workmentioning

confidence: 99%

Fast One-to-Many Multicriteria Shortest Path Search

Kurbanov¹,

Cuchý²,

Vokřínek³

2022

Preprint

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This paper introduces a novel algorithm combination designed for fast one-to-many multicriteria shortest path search. A preprocessing algorithm excludes irrelevant vertices by building a smaller cover graph. A modified version of multicriteria label-setting algorithm operates on the cover graph and employs a dimensionality reduction technique for swifter domination checks. While the method itself maintains solution optimality, it is able to additionally incorporate existing heuristics for further speedups. The proposed algorithm has been tested on multiple criteria combinations of varying correlation. The results show the introduced approach provides a speedup of at least 6 times on simple criteria combinations and up to 60 times on hard instances compared to vanilla multicriteria label-setting. Graph preprocessing also decreases memory requirements of queries by up to 13 times.

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Section: Related Workmentioning

confidence: 99%

Fast One-to-Many Multicriteria Shortest Path Search

Kurbanov¹,

Cuchý²,

Vokřínek³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Reference [21] investigates the evolving ability of a cellular automaton with a type of memory based on the least mean square algorithm. Reference [22] presents a memory-efficient stochastic evolution-based algorithm for solving multiobjective shortest path problems. Reference [23] presents a clonal selection subpixel mapping framework by building a memory cell population.…”

Section: Memory-based Evolutionary Algorithmsmentioning

confidence: 99%

“…Comparing their results and convergence, it is easy to observe the performance advantage of HMPSO over its competitors. The last three rows in Table III indicate that the numbers of functions for which HMPSO are better than CPSO-H6, CLPSO, ALCPSO, FIPS, HPSO-TVAC, EDA-PSO, and PSEDA are 21,18,21,25,22,27, and 27, respectively. The numbers of functions for which they are similar to 1.…”

Section: B Comparison With Seven Pso Algorithmsmentioning

confidence: 99%

Composite Particle Swarm Optimizer With Historical Memory for Function Optimization

Zhang

Jiang

et al. 2015

IEEE Trans. Cybern.

157

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Particle swarm optimization (PSO) algorithm is a population-based stochastic optimization technique. It is characterized by the collaborative search in which each particle is attracted toward the global best position (gbest) in the swarm and its own best position (pbest). However, all of particles' historical promising pbests in PSO are lost except their current pbests. In order to solve this problem, this paper proposes a novel composite PSO algorithm, called historical memory-based PSO (HMPSO), which uses an estimation of distribution algorithm to estimate and preserve the distribution information of particles' historical promising pbests. Each particle has three candidate positions, which are generated from the historical memory, particles' current pbests, and the swarm's gbest. Then the best candidate position is adopted. Experiments on 28 CEC2013 benchmark functions demonstrate the superiority of HMPSO over other algorithms.

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The Multi-Objective Shortest Path Problem with Multimodal Transportation for Emergency Logistics

Guo,

Liu,

Liu

et al. 2024

Mathematics

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The optimization of emergency logistical transportation is crucial for the timely dispatch of aid and support to affected areas. By incorporating practical constraints into emergency logistics, this study establishes a multi-objective shortest path mixed-integer programming model based on a multimodal transportation network. To solve multi-objective shortest path problems with multimodal transportation, we design an ideal point method and propose a procedure for constructing the complete Pareto frontier based on the k-shortest path multi-objective algorithm. We use modified Dijkstra and Floyd multimodal transportation shortest path algorithms to build a k-shortest path multi-objective algorithm. The effectiveness of the proposed multimodal transportation shortest path algorithm is verified using empirical experiments carried out on test sets of different scales and a comparison of the runtime using a commercial solver. The results show that the modified Dijkstra algorithm has a runtime that is 100 times faster on average than the modified Floyd algorithm, which highlights its greater applicability in large-scale multimodal transportation networks, demonstrating that the proposed method both has practical significance and can generate satisfactory solutions to the multi-objective shortest path problem with multimodal transportation in the context of emergency logistics.

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Finding Multi-Objective Shortest Paths Using Memory-Efficient Stochastic Evolution Based Algorithm

Cited by 3 publications

References 24 publications

Fast One-to-Many Multicriteria Shortest Path Search

Fast One-to-Many Multicriteria Shortest Path Search

Composite Particle Swarm Optimizer With Historical Memory for Function Optimization

The Multi-Objective Shortest Path Problem with Multimodal Transportation for Emergency Logistics

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