Hybridization of Racing Methods with Evolutionary Operators for Simulation Optimization of Traffic Lights Programs

Cintrano, Christian; Ferrer, Javier; López-Ibáñez, Manuel; Alba, Enrique

doi:10.1007/978-3-030-72904-2_2

Cited by 3 publications

(8 citation statements)

References 18 publications

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“…The number of vehicles that arrive at their destinations is squared (V (P ) 2 ) to prioritize this criterion over the rest. This fitness function has been successfully used several studies (García-Nieto et al, 2012, 2013Ferrer et al, 2019;Cintrano et al, 2021).…”

Section: The Traffic Light Scheduling Problem (Tlsp)mentioning

confidence: 99%

“…Optimal management of traffic minimizes journey times, reduce fuel consumption and harmful emissions. In some cities, legal and technical limitations preclude real-time traffic light control and mandate an optimal planning of traffic light programs (TLPs) given the particular traffic flows of that city (Teklu et al, 2007;Sánchez et al, 2008;Teo et al, 2010;Sánchez-Medina et al, 2010;García-Nieto et al, 2012;Putha et al, 2012;García-Nieto et al, 2013;Alba, 2014, 2015;Bravo et al, 2016;Ferrer et al, 2016;Péres et al, 2018;Ferrer et al, 2019;Cintrano et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Following this intuition, Cintrano et al (2021) carried out an initial comparison between IRACE, a genetic algorithm, using uniform crossover (Syswerda, 1989) and integer polynomial mutation (Deb and Agrawal, 1999), a differential evolution (DE) algorithm, using the "DE/best/1/bin" strategy (Price et al, 2005), and variants (hybrids) of IRACE replacing its solution generation approach with the evolutionary operators used by the GA and the DE under comparison. Experimental results indicated that, although the original IRACE outperforms the GA and DE algorithms, both hybrids of IRACE+GA and IRACE+DE outperformed IRACE.…”

Section: Introductionmentioning

confidence: 99%

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Hybridization of Evolutionary Operators with Elitist Iterated Racing for the Simulation Optimization of Traffic Lights Programs

Cintrano

Ferrer

López-Ibáñez

et al. 2023

Evolutionary Computation

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In the traffic light scheduling problem the evaluation of candidate solutions requires the simulation of a process under various (traffic) scenarios. Thus, good solutions should not only achieve good objective function values, but they must be robust (low variance) across all different scenarios. Previous work has shown that combining IRACE with evolutionary operators is effective for this task due to the power of evolutionary operators in numerical optimization. In this paper, we further explore the hybridization of evolutionary operators and the elitist iterated racing of IRACE for the simulation-optimization of traffic light programs. We review previous works from the literature to find the evolutionary operators performing the best when facing this problem to propose new hybrid algorithms. We evaluate our approach over a realistic case study derived from the traffic network of Málaga (Spain) with 275 traffic lights that should be scheduled optimally. The experimental analysis reveals that the hybrid algorithm comprising IRACE plus differential evolution offers statistically better results than the other algorithms when the budget of simulations is low. In contrast, IRACE performs better than the hybrids for high simulations budget, although the optimization time is much longer.

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Section: The Traffic Light Scheduling Problem (Tlsp)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybridization of Evolutionary Operators with Elitist Iterated Racing for the Simulation Optimization of Traffic Lights Programs

Cintrano

Ferrer

López-Ibáñez

et al. 2023

Evolutionary Computation

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“…Irace has also been used to reduce the runtime of an exact tree search solver (SPEAR) [3] for the satisfiability problem (SAT) [25]. Moreover, a recent study has integrated the elitist iterated racing method of irace with evolutionary operators for traffic light planning [10].…”

Section: Introductionmentioning

confidence: 99%

Non-Elitist Selection among Survivor Configurations can Improve the Performance of Irace

Ye¹,

Vermetten²,

Doerr³

et al. 2022

Preprint

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Modern optimization strategies such as evolutionary algorithms, ant colony algorithms, Bayesian optimization techniques, etc. come with several parameters that steer their behavior during the optimization process. To obtain high-performing algorithm instances, automated algorithm configuration techniques have been developed. One of the most popular tools is irace, which evaluates configurations in sequential races, at the end of which a statistical test is used to determine the set of survivor configurations. It then selects up to five elite configurations from this set, via greedy truncation selection.We demonstrate that an alternative selection of the elites can improve the performance of irace. Our strategy keeps the best survivor and selects the remaining configurations uniformly at random from the set of survivors. We apply this alternative selection method to tune ant colony optimization algorithms for traveling salesperson problems and to configure an exact tree search solver for satisfiability problems.We also experiment with two non-elitist selection criteria, based on entropy and Gower's distance, respectively. Both methods provide more diverse configurations than irace, making them an interesting approach for exploring a wide range of solutions and understanding algorithms' performance. Moreover, the entropybased selection performs better on our benchmarks than the default selection of irace.

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“…Irace, one of the most popular tools, has shown its ability to improve the performance of the algorithms for various optimization problems [2,7,19,27]. However, we can still intuitively expect to improve the performance of irace considering contemporary optimization techniques.…”

Section: Introductionmentioning

confidence: 99%

Non-elitist Selection Can Improve the Performance of Irace

Vermetten

Doerr

et al. 2022

Lecture Notes in Computer Science

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Modern optimization strategies such as evolutionary algorithms, ant colony algorithms, Bayesian optimization techniques, etc. come with several parameters that steer their behavior during the optimization process. To obtain high-performing algorithm instances, automated algorithm configuration techniques have been developed. One of the most popular tools is irace, which evaluates configurations in sequential races, making use of iterated statistical tests to discard poorly performing configurations. At the end of the race, a set of elite configurations are selected from those survivor configurations that were not discarded, using greedy truncation selection. We study two alternative selection methods: one keeps the best survivor and selects the remaining configurations uniformly at random from the set of survivors, while the other applies entropy to maximize the diversity of the elites. These methods are tested for tuning ant colony optimization algorithms for traveling salesperson problems and the quadratic assignment problem and tuning an exact tree search solver for satisfiability problems. The experimental results show improvement on the tested benchmarks compared to the default selection of irace. In addition, the obtained results indicate that non-elitist can obtain diverse algorithm configurations, which encourages us to explore a wider range of solutions to understand the behavior of algorithms.

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Hybridization of Racing Methods with Evolutionary Operators for Simulation Optimization of Traffic Lights Programs

Cited by 3 publications

References 18 publications

Hybridization of Evolutionary Operators with Elitist Iterated Racing for the Simulation Optimization of Traffic Lights Programs

Hybridization of Evolutionary Operators with Elitist Iterated Racing for the Simulation Optimization of Traffic Lights Programs

Non-Elitist Selection among Survivor Configurations can Improve the Performance of Irace

Non-elitist Selection Can Improve the Performance of Irace

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