A Comparison of Genetic Representations for Multi-objective Shortest Path Problems on Multigraphs

Beke, Lilla; Weiszer, Michal

doi:10.1007/978-3-030-43680-3_3

Cited by 8 publications

(7 citation statements)

References 18 publications

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“…This study extends our previous work [21]. The first main addition compared to [21] is the inclusion of more variants of different representations.…”

Section: Contributionssupporting

confidence: 62%

“…This study extends our previous work [21]. The first main addition compared to [21] is the inclusion of more variants of different representations. Variants include (1) different crossover operators, (2) direct way of encoding parallel edges for the direct variable length representations, and (3) adapting an existing heuristic initialisation method [9] to priority-based representations.…”

Section: Contributionssupporting

confidence: 62%

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A Comparison of Genetic Representations and Initialisation Methods for the Multi-objective Shortest Path Problem on Multigraphs

Beke

Weiszer

2021

SN COMPUT. SCI.

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This paper compares different solution approaches for the multi-objective shortest path problem (MSPP) on multigraphs. Multigraphs as a modelling tool are able to capture different available trade-offs between objectives for a given section of a route. For this reason, they are increasingly popular in modelling transportation problems with multiple conflicting objectives (e.g., travel time and fuel consumption), such as time-dependent vehicle routing, multi-modal transportation planning, energy-efficient driving, and airport operations. The multigraph MSPP is more complex than the NP-hard simple graph MSPP. Therefore, approximate solution methods are often needed to find a good approximation of the true Pareto front in a given time budget. Evolutionary algorithms have been successfully applied for the simple graph MSPP. However, there has been limited investigation of their applications to the multigraph MSPP. Here, we extend the most popular genetic representations to the multigraph case and compare the achieved solution qualities. Two heuristic initialisation methods are also considered to improve the convergence properties of the algorithms. The comparison is based on a diverse set of problem instances, including both bi-objective and triple objective problems. We found that the metaheuristic approach with heuristic initialisation provides good solutions in shorter running times compared to an exact algorithm. The representations were all found to be competitive. The results are encouraging for future application to the time-constrained multigraph MSPP.

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“…This study extends our previous work [21]. The first main addition compared to [21] is the inclusion of more variants of different representations.…”

Section: Contributionssupporting

confidence: 62%

Section: Contributionssupporting

confidence: 62%

A Comparison of Genetic Representations and Initialisation Methods for the Multi-objective Shortest Path Problem on Multigraphs

Beke

Weiszer

2021

SN COMPUT. SCI.

Self Cite

View full text Add to dashboard Cite

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“…Generally genetic operation starts with a selection operator which picks the most suitable chromosomes to produce the ideal solution [13], [14]. Then the operators such as crossover and mutation are applied to prevent early convergence of solutions [15]- [18]. In order to develop a hybrid genetic algorithm to improve the effectiveness of the algorithm we consider Taxi dataset of New York City [19], which includes the pickup time, number of passengers, geo-coordinates, and several other variables.…”

Section: Methodsmentioning

confidence: 99%

A hybrid approach to enhanced genetic algorithm for route optimization problems

Chattoraj

Vinayakamurthy

2023

IJEECS

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Shortest path problem has emerged to be one of the significant areas of research and there are various algorithms involved in it. One of the successful optimization techniques is genetic algorithm (GA). This paper proposes an efficient hybrid genetic algorithm where initially we use a map reduction technique to the graph and then find the shortest path using the conventional genetic algorithm with an improved crossover operator. On comparing this hybrid algorithm with other algorithms, it has been detected that the performance of the modified genetic algorithm is better as comparison to the other methods in terms of various metrics used for the evaluation.

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“…We calculate the shortest path by the fitness function. According to [34,35], there are two main coding schemas of the Genetic algorithm for solving the shortest path, namely, direct and indirect coding. Direct coding is based on the identification number of the node.…”

Section: Genetic Algorithm With Heuristic (Hga)mentioning

confidence: 99%

Counterexample Generation for Probabilistic Model Checking Micro-Scale Cyber-Physical Systems

Liu

Yang

et al. 2021

Micromachines

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Micro-scale Cyber-Physical Systems (MCPSs) can be automatically and formally estimated by probabilistic model checking, on the level of system model MDPs (Markov Decision Processes) against desired requirements in PCTL (Probabilistic Computation Tree Logic). The counterexamples in probabilistic model checking are witnesses of requirements violation, which can provide the meaningful information for debugging, control, and synthesis of MCPSs. Solving the smallest counterexample for probabilistic model checking MDP has been proven to be an NPC (Non-deterministic Polynomial complete) problem. Although some heuristic methods are designed for this, it is usually difficult to fix the heuristic functions. In this paper, the Genetic algorithm optimized with heuristic, i.e., the heuristic Genetic algorithm, is firstly proposed to generate a counterexample for the probabilistic model checking MDP model of MCPSs. The diagnostic subgraph serves as a compact counterexample, and diagnostic paths of MDP constitute an AND/OR tree for constructing a diagnostic subgraph. Indirect path coding of the Genetic algorithm is used to extend the search range of the state space, and a heuristic crossover operator is used to generate more effective diagnostic paths. A prototype tool based on the probabilistic model checker PAT is developed, and some cases (dynamic power management and some communication protocols) are used to illustrate its feasibility and efficiency.

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A Comparison of Genetic Representations for Multi-objective Shortest Path Problems on Multigraphs

Cited by 8 publications

References 18 publications

A Comparison of Genetic Representations and Initialisation Methods for the Multi-objective Shortest Path Problem on Multigraphs

A Comparison of Genetic Representations and Initialisation Methods for the Multi-objective Shortest Path Problem on Multigraphs

A hybrid approach to enhanced genetic algorithm for route optimization problems

Counterexample Generation for Probabilistic Model Checking Micro-Scale Cyber-Physical Systems

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