The Otimization of Traffic Signal Timing for Emergency Evacuation Using the Simulated Annealing Algorithm / Optimalus Šviesoforo Signalo Laiko Nustatymas, Naudojant Modeliuojamąjį Atkaitinimo Algoritmą Avarinės Evakuacijos Metu / Определение Оптимального Времени Сигнала Светофора Во Время Аварийной Эвакуации С Использованием Алгоритма Имитации Отжига

Jahangiri, Arash; Zargari, Shahriar Afandizadeh; Kalantari, Navid

doi:10.3846/16484142.2011.584959

Cited by 21 publications

(3 citation statements)

References 5 publications

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“…Such algorithms solve a problem based on the process of improving a single solution. SA is the commonly used algorithm in this category [25,26]. These five metaheuristic algorithms are all global optimization methods and can solve higher-dimensional problems; they are robust with respect to the complexity of the evaluation of functions.…”

Section: An Overview Of Metaheuristic Algorithmsmentioning

confidence: 99%

A Comparative Study of Four Metaheuristic Algorithms, AMOSA, MOABC, MSPSO, and NSGA-II for Evacuation Planning

et al. 2020

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Evacuation planning is an important activity in disaster management to reduce the effects of disasters on urban communities. It is regarded as a multi-objective optimization problem that involves conflicting spatial objectives and constraints in a decision-making process. Such problems are difficult to solve by traditional methods. However, metaheuristics methods have been shown to be proper solutions. Well-known classical metaheuristic algorithms—such as simulated annealing (SA), artificial bee colony (ABC), standard particle swarm optimization (SPSO), genetic algorithm (GA), and multi-objective versions of them—have been used in the spatial optimization domain. However, few types of research have applied these classical methods, and their performance has not always been well evaluated, specifically not on evacuation planning problems. This research applies the multi-objective versions of four classical metaheuristic algorithms (AMOSA, MOABC, NSGA-II, and MSPSO) on an urban evacuation problem in Rwanda in order to compare the performances of the four algorithms. The performances of the algorithms have been evaluated based on the effectiveness, efficiency, repeatability, and computational time of each algorithm. The results showed that in terms of effectiveness, AMOSA and MOABC achieve good quality solutions that satisfy the objective functions. NSGA-II and MSPSO showed third and fourth-best effectiveness. For efficiency, NSGA-II is the fastest algorithm in terms of execution time and convergence speed followed by AMOSA, MOABC, and MSPSO. AMOSA, MOABC, and MSPSO showed a high level of repeatability compared to NSGA-II. It seems that by modifying MOABC and increasing its effectiveness, it could be a proper algorithm for evacuation planning.

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Section: An Overview Of Metaheuristic Algorithmsmentioning

confidence: 99%

A Comparative Study of Four Metaheuristic Algorithms, AMOSA, MOABC, MSPSO, and NSGA-II for Evacuation Planning

et al. 2020

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“…This process of searching neighbourhood solutions is called a 'move' . However, if the performance of the neighbourhood solution is worse, there is still a chance for the solution to replace the current solution to avoid the problem of being locked into a local optimum (Jahangiri et al 2011). The worse solution will be accepted with a probability ( ) −∆ / Z U e , which depends on two parameters U and ∆Z.…”

Section: Proposed Methodologymentioning

confidence: 99%

Bus route design with limited travel time

2013

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Routing problems are especially important for public transport and especially for urban transport. This research deals with designing a bus route to satisfy the demands of most passengers within a limited total bus travel time. A Simulated Annealing (SA) algorithm is proposed for optimizing the routing design. Before applying the proposed SA algorithm, the Taguchi method is adopted for optimizing the setting of parameters. The experimental results show that the proposed SA algorithm with the optimal parameters setting results in better routes than those designed by other research methods.

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“…It works efficiently on a neighbourhood search within solution space, acceptance probability, and inferior solutions to escape from being trapped in a local mini-mum energy state (Javadian et al 2011). Furthermore, the SAA have been proved to be extremely efficient for solving the hard combinatorial optimization problems (Teghem et al 1995;Jahangiri et al 2011). Thus, we employ this method to solve the proposed model founded in this paper.…”

Section: Algorithmmentioning

confidence: 99%

Optimization of Ship Speed and Fleet Deployment Under Carbon Emissions Policies for Container Shipping

Xing

Yang

et al. 2019

Transport

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In this paper, under the consideration of two carbon emissions policies, the issues of optimizing ship speed and fleet deployment for container shipping were addressed. A mixed-integer nonlinear programming model of ship speed and fleet deployment was established with the objective of minimising total weekly operating costs. A simulated annealing algorithm was proposed to solve the problem. An empirical analysis was conducted with the data selected from the benchmark suite. The applicability and effectiveness of the established model and its algorithm are verified by the results. According to the results, two policies of the cap-and-trade programme and the carbon tax can better optimize the results of the ship speed and fleet deployment problem to achieve the goal of reducing carbon emissions. The research remarks in this paper will provide a solution for container shipping companies to make optimized decisions under carbon emissions policies.

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Cited by 21 publications

References 5 publications

A Comparative Study of Four Metaheuristic Algorithms, AMOSA, MOABC, MSPSO, and NSGA-II for Evacuation Planning

A Comparative Study of Four Metaheuristic Algorithms, AMOSA, MOABC, MSPSO, and NSGA-II for Evacuation Planning

Bus route design with limited travel time

Optimization of Ship Speed and Fleet Deployment Under Carbon Emissions Policies for Container Shipping

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