A Multi-Objective Agent-Based Control Approach With Application in Intelligent Traffic Signal System

For the purpose of improving the efficiency of traffic signal control for isolate intersection under oversaturated conditions, a multi-objective optimization algorithm for traffic signal control is proposed. Throughput maximum and average queue ratio minimum are selected as the optimization objectives of the traffic signal control under oversaturated condition. A simulation environment using VISSIM SCAPI was utilized to evaluate the convergence and the optimization results under various settings and traffic conditions. It is written by C++/CRL to connect the simulation software VISSIM and the proposed algorithm. The simulation results indicated that the signal timing plan generated by the proposed algorithm has good efficiency in managing the traffic flow at oversaturated intersection than the commonly utilized signal timing optimization software Synchro. The update frequency applied in the simulation environment was 120 s, and it can meet the requirements of signal timing plan update in real filed. Thus, the proposed algorithm has the capability of searching Pareto front of the multi-objective problem domain under both normal condition and over-saturated condition.

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Bi-level optimization for eco-traffic signal system

2016 International Conference on Connected Vehicles and Expo (ICCVE) ◽

10.1109/iccve.2016.6 ◽

2016 ◽

Cited By ~ 4

Author(s):

Hojin Jung ◽

Saerona Choi ◽

Byungkyu Brian Park ◽

Haengju Lee ◽

Sang Hyuk Son

Keyword(s):

Traffic Signal ◽

Signal System

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Multi-agent collaborative search: an agent-based memetic multi-objective optimization algorithm applied to space trajectory design

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410011410274 ◽

2011 ◽

Vol 225 (11) ◽

pp. 1211-1227 ◽

Cited By ~ 26

Author(s):

M Vasile ◽

F Zuiani

Keyword(s):

Selection Criterion ◽

Search Space ◽

Trajectory Design ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Agent Based ◽

Multi Objective ◽

Collaborative Search ◽

Multi Agent ◽

Space Trajectory Design

This article presents an algorithm for multi-objective optimization that blends together a number of heuristics. A population of agents combines heuristics that aim at exploring the search space both globally and in a neighbourhood of each agent. These heuristics are complemented with a combination of a local and global archive. The novel agent-based algorithm is tested at first on a set of standard problems and then on three specific problems in space trajectory design. Its performance is compared against a number of state-of-the-art multi-objective optimization algorithms that use the Pareto dominance as selection criterion: non-dominated sorting genetic algorithm (NSGA-II), Pareto archived evolution strategy (PAES), multiple objective particle swarm optimization (MOPSO), and multiple trajectory search (MTS). The results demonstrate that the agent-based search can identify parts of the Pareto set that the other algorithms were not able to capture. Furthermore, convergence is statistically better although the variance of the results is in some cases higher.

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