Multiobjective Optimization of Evacuation Routes in Stadium Using Superposed Potential Field Network Based ACO

Kou, Jialiang; Xiong, Shengwu; Fang, Zhixiang; Zong, Xinlu; Chen, Zhong

doi:10.1155/2013/369016

Cited by 14 publications

(6 citation statements)

References 24 publications

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“…An extra dimension that could to be recognised is the individual variation (or heterogeneity) in strategy choosing that also could be subjected to tests of optimality using numerical analyses [79][80][81]. This stream of behavioural optimisation studies can be regarded as a complementary domain to that of the architectural optimisations that seek to facilitate evacuations through physical modifications of the environment (as opposed to the behaviour of evacuees) [82][83][84][85] or the approach of route-planning optimisations [67,[86][87][88][89][90][91][92][93][94].…”

Section: Summary Discussion and Future Researchmentioning

confidence: 99%

‘Rationality’ in Collective Escape Behaviour: Identifying Reference Points of Measurement at Micro and Macro Levels

Haghani

Sarvi

2019

Journal of Advanced Transportation

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Background. Evacuation behaviour of human crowds is often characterised by the notion of ‘irrational behaviour’. While the term has been frequently used in the literature, clear definitions and methods for measuring rationality do not exist. Objective. Here, we suggest that rationality, in this context, can alternatively and more effectively be formulated as a question of ‘optimal behaviour’. Decision optimality can potentially be measured and quantified. The main challenges, however, include (i) distinctly identifying the level at which we measure optimality, and (ii) identifying proper reference points at each level. Methods. We differentiate between optimality at the individual (i.e., micro) and the system (i.e., macro/aggregate) levels and illustrate how certain reference points can be established at each level. We suggest that, at the micro level, optimality of individual decisions can be quantified by comparing the outcome of each individual’s decision to those of their ‘nearly equal peers’. At the macro level, optimality can be measured by simulating the system using parametric numerical models and measuring the system performance while altering the behavioural parameters compared to their empirical estimates. Results. Having applied these methods, we observed that variation in micro level decision optimality rises rapidly as the space becomes more heavily crowded. As crowdedness increases in the environment, the difference between ‘good’ and ‘bad’ decisions becomes more distinct; and suboptimal decisions become more frequent. In other words, optimality at individual level seems to be moderated by the level of crowdedness. At the macro level, numerical simulations showed that, for certain exit attributes (like exit congestion), extreme marginal valuations (or preferences) were optimal, whereas for certain other attributes (like exit visibility), intermediate levels of valuation were closer to the optimal. In most cases, the natural observed (or estimated) tendency of evacuees (at the aggregate level) was not quite at the optimum level, meaning that the system could improve by modifying individuals’ marginal valuations of exit attributes. Applications and Recommendations. These results highlight the importance of guiding evacuation decisions particularly in heavily crowded spaces. They also theoretically illustrate the potential benefit of influencing/modifying people’s evacuation strategies, so they make decisions that are collectively more efficient. A crucial step to this end, however, is to identify what optimum strategy is and under what circumstances people are likelier to make suboptimal decisions.

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Section: Summary Discussion and Future Researchmentioning

confidence: 99%

‘Rationality’ in Collective Escape Behaviour: Identifying Reference Points of Measurement at Micro and Macro Levels

Haghani

Sarvi

2019

Journal of Advanced Transportation

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“…Court. During the investigation, it was found that the lighting methods of the school's gymnasium badminton court mainly include complete natural lighting, combination of natural lighting and artificial lighting, and complete artificial lighting [16][17][18][19]. In most cases, the badminton court in the gymnasium of our school only relies on natural lighting and artificial lighting.…”

Section: Illumination Data Analysis Of Each Area Of the Badmintonmentioning

confidence: 99%

The Comfort Analysis Model Design of the Ground Light Environment of a Dome Reflective Badminton Court in a University

Ding

Wang

Wu³

2022

Scientific Programming

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School sports is an important part of sports. The participants are mainly school students. Students play an important role in the development of the country. The development of campus sports is particularly important for the future development of the country. With the continuous improvement of the level of sports technology, to achieve higher, faster, and stronger sports goals, the requirements for sports venues continue to increase. To study whether the environment of the school’s badminton court meets the requirements of the school’s teachers and students to exercise and whether it can achieve the purpose of improving their own sports skills, the above problems are studied. Through the analysis of the light environment in the school grounds, to judge some problems existing in the badminton field of the school, improvement measures are put forward through research, and feasibility analysis of the improvement measures is carried out.

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“…In both technical practice and theoretical research, there are a large number of multi-objective optimization (MOP) problems that need to be optimized synchronously for multiple objectives [1][2][3]. When solving multi-objective optimization problems, the traditional method is to convert this problem into a single-objective problem based on prior knowledge and to use single-objective optimization methods to obtain a satisfactory Pareto optimal solution [4].…”

Section: Introductionmentioning

confidence: 99%

Using the Decomposition-Based Multi-Objective Evolutionary Algorithm with Adaptive Neighborhood Sizes and Dynamic Constraint Strategies to Retrieve Atmospheric Ducts

Mai

Shi

Liao

et al. 2020

Sensors

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The traditional method of retrieving atmospheric ducts is to use the special sensor of weather balloons or rocket soundings to obtain information intelligently, and it is very expensive. Today, with the development of technology, it is very convenient to retrieve the atmospheric ducts from Global Navigation Satellite System (GNSS) phase delay and propagation loss observation data, and then the GNSS receiver on the ground forms an automatic receiving sensor. This paper proposes a hybrid decomposition-based multi-objective evolutionary algorithm with adaptive neighborhood sizes (EN-MOEA/ACD-NS), which dynamically imposes some constraints on the objectives. The decomposition-based multi-objective evolutionary algorithm (MOEA/D) updates the solutions through neighboring objectives, the number of which affects the quality of the optimal solution. Properly constraining the optimization objectives can effectively balance the diversity and convergence of the population. The experimental results from the Congress on Evolutionary Computation (CEC) 2009 on test instances with hypervolume (HV), inverted generational distance (IGD), and average Hausdorff distance ∆ 2 metrics show that the new method performs similarly to the evolutionary algorithm MOEA/ACD-NS, which considers only the dynamic change of the neighborhood sizes. The improved algorithm is applied to the practical problem of jointly retrieving atmospheric ducts with GNSS signals, and its performance further demonstrates its feasibility and practicability. the multi-objective optimization can make full use of multi-source observation information and can improve the reliability of the solution. The traditional method can only obtain a Pareto optimal solution that is not necessarily satisfactory to the decision-maker; the rationality of prior knowledge also affects the selection of the optimal solution. This paper proposes a multi-objective evolutionary algorithm (MOEA) for MOP problems in order to obtain a set of Pareto optimal solutions [5,6]. According to the optimization framework, MOEAs can be roughly divided into three categories, namely: (a) Pareto-based approaches [7], (b) indicator-based approaches [8], and (c) decomposition-based approaches [9].Pareto-based methods sort the individuals according to the dominant relationship among them, and then the optimal solution is selected. The Pareto-based approach is the most popular method in the evolutionary multi-objective optimization community. NSGA-II and SPEA2 are the most representative algorithms, and they are very effective when dealing with two or three objective functions [7,10]. However, when the number of objective functions increases to more than three, most of the solutions in the NSGA-II and SPEA2 search spaces become non-dominated solutions, resulting in a rapid decline in search capabilities.Indicator-based MOEAs use the value of performance indicators to guide the search direction, which drives whole generated solutions to the Pareto solution sets. As the calculation of the Pareto dominance relatio...

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Multiobjective Optimization of Evacuation Routes in Stadium Using Superposed Potential Field Network Based ACO

Cited by 14 publications

References 24 publications

‘Rationality’ in Collective Escape Behaviour: Identifying Reference Points of Measurement at Micro and Macro Levels

‘Rationality’ in Collective Escape Behaviour: Identifying Reference Points of Measurement at Micro and Macro Levels

The Comfort Analysis Model Design of the Ground Light Environment of a Dome Reflective Badminton Court in a University

Using the Decomposition-Based Multi-Objective Evolutionary Algorithm with Adaptive Neighborhood Sizes and Dynamic Constraint Strategies to Retrieve Atmospheric Ducts

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