Traffic Light Signal Parameters Optimization using Modification of Multielement Genetic Algorithm

Wijaya, I Gede Pasek Suta; Uchimura, Keeichi; Koutaki, Gou

doi:10.11591/ijece.v8i1.pp246-253

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…These include for example: Robotics [18], Mathematics [19], Electric distribution systems [20], Traffic Light Signal Parameters Optimization [21], etc. In the next section, we present an application of the GA to the optimal design of the 4th order band-pass filter.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Sizing of 4th order Active Band-Pass Filter based on Multiple Feed-Back Topology by using the Genetic Algorithm

Asmae¹

2020

IJATCSE

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In this paper, we deal with the design of an active fourth order Butterworth bandpass filter for Radio Frequency IDentification (RFID) system reader to reject all signals outside the band (10-20) kHz and to amplify the low antenna signal with a center frequency of 15 kHz. The Multiple Feed-Back (MFB) topology is used to implement this filter. The values of the passive components, forming the studied filter, are selected from manufactured series, this selection is one of the most crucial design steps in analog active filter design problems. Thus, intelligent search methods must be performed for a fast and optimum selection. In this study, we focus on the use of the Genetic Algorithm (GA) for the optimal design of the 4th order Band Pass Filter, accompanied with a sensitivity analysis to determine the most influential components in the considered filter. SPICE simulations supported by Monte Carlo analysis are used to validate the obtained results/performance.

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Section: Genetic Algorithmmentioning

confidence: 99%

Sizing of 4th order Active Band-Pass Filter based on Multiple Feed-Back Topology by using the Genetic Algorithm

Asmae¹

2020

IJATCSE

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“…Traffic control is one of the important tools to contain the congestion, restrain traffic flow and reduce emissions. Many researchers have proposed adaptive traffic signal control (ATSC) to solve the traffic congestion problem using a variety of optimization techniques, such as heuristics [2]- [4], evolutionary algorithms [5], [6] and self-organization strategy [7].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary reinforcement learning multi-agents system for intelligent traffic light control: new approach and case of study

Basmassi

Boudaakat²,

Chentoufi³

et al. 2022

IJECE

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<span>Due to the rapid growth of urban vehicles, traffic congestion has become more serious. The signalized intersections are used all over the world and still established in the new construction. This paper proposes a self-adapted approach, called evolutionary reinforcement learning multi-agents system (ERL-MA), which combines computational intelligence and machine learning. The concept of this work is to build an intelligent agent capable of developing senior skills to manage the traffic light control system at any type of junction, using two powerful tools: learning from the confronted experience and the assumption using the randomization concept. The ERL-MA is an independent multi-agents system composed of two layers: the modeling and the decision layers. The modeling layer uses the intersection modeling using generalized fuzzy graph technique. The decision layer uses two methods: the novel greedy genetic algorithm (NGGA), and the Q-learning. In the Q-learning method, a multi Q-tables strategy and a new reward formula are proposed. The experiments used in this work relied on a real case of study with a simulation of one-hour scenario at Pasubio area in Italy. The obtained results show that the ERL-MA system succeeds to achieve competitive results comparing to urban traffic optimization by integrated automation (UTOPIA) system using different metrics.</span>

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“…Currently, the attention of using the metaheuristic algorithms in solving engineering problems had increased significantly. These due to they are derivative-free, impose no restrictions on the problem formulation, and can be easily adapted for various real world problems [1][2][3][4][5][6][7]. Proton Exchange Membrane Fuel Cell (PEMFC) is a hydrogen based fuel cell.…”

Section: Introductionmentioning

confidence: 99%

Optimizing PEMFC model parameters using ant lion optimizer and dragonfly algorithm: A comparative study

Isa¹,

Nayan²,

Arshad³

et al. 2019

IJECE

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This paper introduced two optimization algorithms which are Ant Lion Optimizer (ALO) and Dragonfly Algorithm (DA) for extracting the Proton Exchange Membrane Fuel Cell (PEMFC) polarization curve parameters. The results produced by both algorithms are being compared to observe their performance. As a results, the ALO shows great performance compared to DA. Furthermore, these results also being compared with the results of the other reported metaheuristics algorithms. The ALO and DA presented competitive results.

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Traffic Light Signal Parameters Optimization using Modification of Multielement Genetic Algorithm

Cited by 5 publications

References 14 publications

Sizing of 4th order Active Band-Pass Filter based on Multiple Feed-Back Topology by using the Genetic Algorithm

Sizing of 4th order Active Band-Pass Filter based on Multiple Feed-Back Topology by using the Genetic Algorithm

Evolutionary reinforcement learning multi-agents system for intelligent traffic light control: new approach and case of study

Optimizing PEMFC model parameters using ant lion optimizer and dragonfly algorithm: A comparative study

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