Kaizhou Gao

Optimizing urban traffic light scheduling problem using harmony search with ensemble of local search

Display Omitted A centralized model for urban traffic light scheduling problem (UTLSP).A discrete harmony search algorithm (DHS) for UTLSP.An ensemble of three local search operator to improve performance of DHS.Extensive experimental comparisons and discussion to verify DHS with ensemble. This study addresses urban traffic light scheduling problem (UTLSP). A centralized model is employed to describe the urban traffic light control problem in a scheduling framework. In the proposed model, the concepts of cycles, splits, and offsets are not adopted, making UTLSP fall in the class of model-based optimization problems, where each traffic light is assigned in a real-time manner by the network controller. The objective is to minimize the network-wise total delay time in a given finite horizon. A swarm intelligent algorithm, namely discrete harmony search (DHS), is proposed to solve the UTLSP. In the DHS, a novel new solution generation strategy is proposed to improve the algorithms performance. Three local search operators with different structures are proposed based on the feature of UTLSP to improve the performance of DHS in local space. An ensemble of local search methods is proposed to integrate different neighbourhood structures. Extensive computational experiments are carried out using the traffic data from partial traffic network in Singapore. The DHS algorithm with and without local search operators and ensemble is evaluated and tested. The comparisons and discussions verify the effectiveness of DHS algorithms with local search operators and ensemble for solving UTLSP.

Jaya, harmony search and water cycle algorithms for solving large-scale real-life urban traffic light scheduling problem

Abstract This paper studies a large-scale urban traffic light scheduling problem (LUTLSP). A centralized model is developed to describe the LUTLSP, where each outgoing flow rate is described as a nonlinear mixed logical switching function over the source link’s density, the destination link’s density and capacity, and the driver’s potential psychological response to the past traffic light signals. The objective is to minimize the total network-wise delay time of all vehicles in a time window. Three metaheuristic optimization algorithms, named as Jaya algorithm, harmony search (HS) and water cycle algorithm (WCA) are implemented to solve the LUTLSP. Since we adopt a discrete-time formulation of LUTLSP, we firstly develop a discrete version of Jaya and WCA. Secondly, some improvement strategies are proposed to speed up the convergence of applied optimizers. Thirdly, a feature based search operator is utilized to improve the search performance of reported optimization methods. Finally, experiments are carried out based on the real traffic data in Singapore. The HS, WCA, Jaya, and their variants are evaluated by solving 11 cases of traffic networks. The comparisons and discussions verify that the considered metaheuristic optimization methods can effectively solve the LUTLSP considerably surpassing the existing traffic light control strategy.

Urban road traffic light real-time scheduling

This paper addresses urban traffic signal control in a scheduling framework, where the dynamics of an urban traffic network controlled by traffic lights is described by a novel model, which inserts mixed logical constraints into a cell transmission flow dynamic model, capable of capturing the nonlinear relationship between each outgoing link flow rate and the corresponding upstream and downstream link capacities and the past traffic light signals. With a control goal of minimizing the total network-wise delay time, we translate the traffic signal control problem into a centralized mixed integer linear programming problem solvable by several existing tools, e.g., CPLEX. To overcome the potentially high complexity involved in the centralized approach, we propose a distributed scheduling strategy based on Lagrangian relaxation and subgradient method. Simulation results are provided to demonstrate the effectiveness of the proposed traffic light scheduling approach.

A decomposition-based multi-objective evolutionary algorithm with quality indicator

Abstract The issue of integrating preference information into multi-objective optimization is considered, and a multi-objective framework based on decomposition and preference information, called indicator-based MOEA/D (IBMOEA/D), is presented in this study to handle the multi-objective optimization problems more effectively. The proposed algorithm uses a decomposition-based strategy for evolving its working population, where each individual represents a subproblem, and utilizes a binary quality indicator-based selection for maintaining the external population. Information obtained from the quality improvement of individuals is used to determine which subproblem should be invested at each generation by a power law distribution probability. Thus, the indicator-based selection and the decomposition strategy can complement each other. Through the experimental tests on seven many-objective optimization problems and one discrete combinatorial optimization problem, the proposed algorithm is revealed to perform better than several state-of-the-art multi-objective evolutionary algorithms. The effectiveness of the proposed algorithm is also analyzed in detail.

Improved artificial bee colony algorithm for solving urban traffic light scheduling problem

In this paper, a novel centralized traffic network model is proposed to describe the urban traffic light scheduling problem (UTLSP) in a traffic network. The objective is to minimize the network-wise total delay time of all vehicles in a fixed time window. To overcome the potentially high computational complexity involved in UTLSP, an improved artificial bee colony (IABC) algorithm is proposed. A new solution generating strategy and three local search operators corresponding to different neighbourhood structures of UTLSP are proposed to improve the performance of IABC. Extensive computational experiments are carried out using sixteen instances with different problem-scales. The IABC with and without three local search operators are evaluated and compared. The comparisons and discussions show the competitiveness of IABC for solving UTLSP.

Jaya algorithm for solving urban traffic signal control problem

This paper studies a large-scale urban traffic signal control problem (LUTSCP). A centralized model is developed for describing the LUTSCP in a scheduling framework. The objective is to minimize the total network-wise delay in a fixed time window. We have implemented a recently developed algorithm, so called Jaya, to solve the LUTSCP. The population initialization is based on the four stages of traffic signal in Singapore. A simple new solution generation strategy is proposed to improve the performance of the Jaya. A neighborhood search operator is proposed based on the characteristics of LUTSCP to improve the search performance in local search space. Experiments are carried out using the traffic signal data from Singapore traffic network. The performance of the new strategy for generating feasible solution and the neighborhood search operator are evaluated and discussed. The optimization results obtained by standard Jaya algorithm and its variants are compared to those by existing traffic signal control system. The comparisons and discussions verify that the Jaya algorithm and its variants are superior over the existing traffic light control. In future work, we will compare the performance of Jaya algorithm to existing intelligent algorithms in literature.

Discrete harmony search algorithm for solving urban traffic light scheduling problem

This study addresses the urban traffic light scheduling problem (UTLSP). A novel centralized model is proposed to describe the urban traffic light control problem in a scheduling framework. The objective is to minimize the total network-wise delay in a fixed time window. A discrete harmony search algorithm (DHS) is proposed to solve the UTLSP. A novel new solution generation strategy is proposed to improve the performance of DHS. A dynamic local search approach is proposed based on the characteristics of UTLSP to improve the search performance in local space. The population initialization is based on the four traffic light stages in Singapore. Extensive computational experiments are carried out using the traffic data in some area of Singapore. The dynamic local search approach is evaluated and tested. The results by DHS algorithm are compared to those by existing traffic light control system. The comparisons and discussions verify that the DHS algorithm is better than the existing traffic light control.

Traffic light scheduling for pedestrians and vehicles

This paper presents a traffic signal scheduling strategy with consideration of both pedestrians and vehicles in the urban traffic system. Firstly, a novel mathematical model consisting of several logic constraints is proposed to describe the pedestrian flow in the urban traffic network and its dynamics are developed based on the crossing rules. Secondly, a mathematical model about the vehicle traffic network is introduced. Thirdly, a traffic light scheduling strategy to minimize the trade-off of the delays between pedestrians and vehicles is proposed. Finally, we translate this traffic signal scheduling problem to a mixed integer quadratic programming (MIQP) problem which can be solved by several existing tools, e.g., GUROBI. Numerical simulation results are provided to illustrate the effectiveness of our real-time traffic light scheduling for pedestrian movement and the potential impact to the vehicle traffic flows by the pedestrian movement.

Discrete Jaya algorithm for flexible job shop scheduling problem with new job insertion

This paper researches on the flexible job shop scheduling problem (FJSP) with new job insertion. FJSP with new job insertion includes two phases: initializing schedules and rescheduling after new job(s) insertion. Initializing schedules is the standard FJSP problem while rescheduling is an FJSP with different job start time and different machine start time. The objective is to minimize maximum machine workload. A recently developed algorithm, so called Jaya, is employed to solve the FJSP with new job insertion and a discrete version of Jaya is proposed. Extensive computational experiments are carried out on eight real instances from remanufacturing enterprise. The discrete Jaya is compared to several existing heuristics and ensemble of them for FJSP with new job insertion. The results and comparisons verify that the discrete Jaya algorithm is superior over the existing methods. In future work, we will future improve the performance of discrete Jaya and compare it to more existing intelligent algorithms in literature.

Improved model of combinatorial Internet shopping optimization problem using evolutionary algorithms

Online shopping has become an essential part of our life, which provides a suitable, cheap, and quick way for customers to enjoy a wide variety of products. However, due to the large number of online stores, a customer usually faces difficulties to review all available offers manually in order to find a favorite item. The Internet shopping optimization problem (ISOP) is a multiple-item multiple-shop optimization problem, which targets to minimize the total cost for a costumer to purchase a given set of products over all available offers. In this paper, the mathematical model of existing ISOP has been improved. In the improved model of ISOP different constraints and assumptions such as the maximum budget, discounts offered by internet shops have been taken into account. Several metaheuristic optimization methods such as the genetic algorithm are implemented. The obtained numerical results illustrate the effectiveness of the improved model and metaheuristics applied.