Comparative evaluation of heuristic optimization methods in urban arterial network optimization

Agbolosu-Amison, Seli; Park, Byungkyu; Yun, Ilsoo

doi:10.1109/itsc.2009.5309710

Cited by 11 publications

(8 citation statements)

References 11 publications

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“…The result is that many signal optimization efforts are likely experiencing premature convergence on mediocre solutions, because they involved ineffective methods and/or parameters. Some traffic signal timing studies (Oda et al, 1996) have shown genetic algorithms to be more effective than hill-climbing methods, and one signal timing study (Agbolosu-Amison et al, 2009) has shown genetic algorithms to be more effective than Harmony search. However no known traffic signal timing studies have examined many other available search methods and parameters.…”

Section: Heuristic Search Methods Used For Traffic Signal Timingmentioning

confidence: 99%

Optimality versus run time for isolated signalized intersections

Hale¹,

Park²,

Stevanovic³

et al. 2015

Transportation Research Part C: Emerging Technologies

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Section: Heuristic Search Methods Used For Traffic Signal Timingmentioning

confidence: 99%

Optimality versus run time for isolated signalized intersections

Hale¹,

Park²,

Stevanovic³

et al. 2015

Transportation Research Part C: Emerging Technologies

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“…have been proposed, as in survey papers and recent books [3,12,[25][26][27][28]. Recent papers [29][30][31] show the comparative evaluations of the recent meta-heuristic optimizer. To add the state-ofthe-art, we inspired from the evolution theory of plant genetics based on Mendel's inheritance law to propose a genetically evolved optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Mendelian Evolutionary Theory Optimization Algorithm

Gupta¹,

Khosravy²,

Patel³

et al. 2020

Preprint

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<div>This study presented a new multi-species binary coded algorithm, Mendelian Evolutionary Theory Optimization (METO), inspired by the plant genetics. This framework mainly consists of three concepts: First, the “denaturation” of DNA’s of two different species to produce the hybrid “offspring DNA”. Second , the Mendelian evolutionary theory of genetic inheritance, which explains how the dominant and recessive traits appear in two successive generations. Third, the Epimuation, through which organism resist for natural mutation. The above concepts are reconfigured in order to design the binary meta-heuristic evolutionary search technique. Based on this framework, four evolutionary operators – 1) Flipper, 2) Pollination, 3) Breeding, and 4) Epimutation – are created in the binary domain. In this paper, METO is compared with well-known evolutionary and swarm optimizers 1) Binary Hybrid GA (BHGA), 2) Bio-geography Based Optimization (BBO), 3) Invasive Weed Optimization (IWO), 4) Shuffled Frog Leap Algorithm (SFLA), 5) Teaching-Learning Based Optimization (TLBO), 6) Cuckoo Search (CS), 7) Bat Algorithm (BA), 8) Gravitational Search Algorithm (GSA), 9) Covariance Matrix Adaptation Evolution Strategy(CMAES), 10) Differential Evolution (DE), 11) Firefly Algorithm (FA) and 12) Social Learning PSO (SLPSO). This comparison is evaluated on 30 and 100 variables benchmark test functions, including noisy, rotated, and hybrid composite functions. Kruskal Wallis statistical rank-based non-parametric H-test is utilized to determine the statistically significant differences between the output distributions of the optimizer, which are the result of the 100 independent runs. The statistical analysis shows that METO is a significantly better algorithm for complex and multi-modal problems with many local extremes.</div>

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“…Christofa monitored the vehicle queue status and the movement of the end-of-queue using connected vehicle information and IntelliDrive data [1]. Agbolosu Amison proposed a dynamic signal optimization method by detecting the gap between clusters of vehicles [2]. He used online data to detect vehicles platoons and then optimized the signal using mixed integer linear program [3].…”

Section: Introductionmentioning

confidence: 99%

A Fast Method to Prevent Traffic Blockage by Signal Control Based on Reinforcement Learning

Shen¹

2017

Proceedings of the International Conference on Communication and Electronic Information Engineering (CEIE 2016)

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In this paper, we present an efficient and fast way to prevent traffic blockage by controlling traffic signal. A new model is adopted to out program which is fused and developed by probabilistic model and cellular automatic model (CA model). Based on this model, we used wavelet neural network (WNN) for predicting traffic flow and use this data to improve the green or red light time sitting. Q-learning algorithm, as one of the reinforcement learning methods, also is applied to project the phase of traffic light from different intersections. Considering the requirement of model response rate, we also develop this algorithm for a fast respond to the complex and fickle traffic condition. Finally, a simulation study is carried out to evaluate out method and the result shows that this method can avoid traffic blockage efficiently.

show abstract

Comparative evaluation of heuristic optimization methods in urban arterial network optimization

Cited by 11 publications

References 11 publications

Optimality versus run time for isolated signalized intersections

Optimality versus run time for isolated signalized intersections

Mendelian Evolutionary Theory Optimization Algorithm

A Fast Method to Prevent Traffic Blockage by Signal Control Based on Reinforcement Learning

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