A memory efficient stochastic evolution based algorithm for the multi-objective shortest path problem

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

doi:10.1016/j.asoc.2013.09.008

Cited by 14 publications

(13 citation statements)

References 12 publications

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“…For example, the work in [18] presents a memory mechanism by introducing memory modules into a membrane algorithm for solving knapsack problems. Reference [19] presents a stochastic evolution algorithm for solving multiobjective shortest path problems by using memory efficiently. Reference [20] presents a modified harmony search algorithm together with a new memory consideration scheme based on a roulette wheel mechanism.…”

Section: Memory-based Evolutionary 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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Section: Memory-based Evolutionary Algorithmsmentioning

confidence: 99%

Composite Particle Swarm Optimizer With Historical Memory for Function Optimization

Zhang

Jiang

et al. 2015

IEEE Trans. Cybern.

157

View full text Add to dashboard Cite

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“…A combined score is proposed for each unique path based on , the normalized values of (between 0 and 1) and the normalized values of (between 0 and 1). The normalization of and are summarized in equations (4) and (5).…”

Section: Inputmentioning

confidence: 99%

“…Finding the path through a stochastic network where the arc attribute is not deterministic, makes it difficult to find the quickest path. Algorithmic and heuristic approaches have been developed in the past for finding the best path in a network with stochastic arc costs and some of the recent publications include [2], [3], [4], [5], [6] and [7].…”

Section: Introductionmentioning

confidence: 99%

Finding the robust quickest path for call-taxis in a city road network

Godwin

2015

2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

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The increased number of vehicles on city roads has prompted a new type of transportation logistics problem of finding the quickest path for call-taxis through a city road network, where the travel times are stochastic. The stochastic nature of travel times are caused by various obstacles in a road network such as traffic signals and speed breakers. A Monte-Carlo simulation based approach is developed to identify unique quickest paths between two locations in a stochastic road network. A multi-criteria score is proposed to identify the time robust path among the unique paths. The proposed solution approach can be embedded in the navigation system of call-taxis to help them identify a path that has a higher likelihood of reaching a location on time.

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“…During the process of path planning, we need to take into account the constraints on the performances of devices and environmental limits etc. Currently, the mainly used algorithms on path planning include the improved Dijkstra [3], genetic algorithm [4], outer approximation algorithm [5], stochastic evolution based algorithm [6] and ant colony algorithm [7]. We propose an algorithm in this paper, which is based on a path network, to obtain the shortest trajectory for a hex-rotor aircraft in complex terrains with no-fly zones.…”

Section: Introductionmentioning

confidence: 99%

Indoor path planning for hex-rotor aircraft with landmark-based visual navigation

Shi

Wang

Cui

et al. 2015

2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

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In this paper, a indoor path planning algorithm is presented to obtain the shortest trajectory for the hex-rotor aircraft in the complex terrains with no-fly zones. This algorithm finds all flyable and feasible trajectories from the start point to the landmarks and from the landmarks to the destination point firstly. Then, it constructs the path network between all landmarks and connects the start point, all landmarks and the destination point. Finally, it finds the final navigable trajectory. To overcome the drawbacks of Global Positioning System (GPS) and improve the positioning accuracy, the visual navigation based on landmarks is employed to assist the Inertial Navigation System (INS). It is accomplished by comparing the corresponding position of landmarks in the real-time image and onboard stored integral referenced image. The landmarks must be visible and distinguishable. The results of simulations and actual indoor flights show that the algorithm proposed in this paper was feasible for path planning and it can be used in indoor and outdoor environments.

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A memory efficient stochastic evolution based algorithm for the multi-objective shortest path problem

Cited by 14 publications

References 12 publications

Composite Particle Swarm Optimizer With Historical Memory for Function Optimization

Composite Particle Swarm Optimizer With Historical Memory for Function Optimization

Finding the robust quickest path for call-taxis in a city road network

Indoor path planning for hex-rotor aircraft with landmark-based visual navigation

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