Ali Sadollah

Artificial bee colony algorithm for scheduling and rescheduling fuzzy flexible job shop problem with new job insertion

Abstract This study addresses flexible job shop scheduling problem (FJSP) with two constraints, namely fuzzy processing time and new job insertion. The uncertainty of processing time and new job insertion are two scheduling related characteristics in remanufacturing. Fuzzy processing time is used to describe the uncertainty in processing time. Rescheduling operator is executed when new job(s) is (are) inserted into the schedule currently being executed. A two-stage artificial bee colony (TABC) algorithm with several improvements is proposed to solve FJSP with fuzzy processing time and new job insertion constraints. Also, several new solution generation methods and improvement strategies are proposed and compared with each other. The objective is to minimize maximum fuzzy completion time. Eight instances from remanufacturing are solved using the proposed TABC algorithm. The proposed improvement strategies are compared and discussed in detail. Two proposed ABC algorithms with the best performances are compared against seven existing algorithms over by five benchmark cases. The optimization results and comparisons show the competitiveness of the proposed TABC algorithm for solving FJSP.

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.

Fuzzy Dynamic Adaptation of Parameters in the Water Cycle Algorithm

This paper describes the enhancement of the water cycle algorithm (WCA) using a fuzzy inference system to dynamically adapt its parameters. The original WCA is compared in terms of performance with the proposed method called WCA with dynamic parameter adaptation (WCA-DPA). Simulation results on a set of well-known test functions show that the WCA is improved with a fuzzy dynamic adaptation of the parameters.

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.

Water Cycle Algorithm with Fuzzy Logic for Dynamic Adaptation of Parameters

This paper describes the enhancement of the Water Cycle Algorithm (WCA) using a fuzzy inference system to adapt its parameters dynamically. The original WCA is compared regarding performance with the proposed method called Water Cycle Algorithm with Dynamic Parameter Adaptation (WCA-DPA). Simulation results on a set of well-known test functions show that the WCA can be improved with a fuzzy dynamic adaptation of the parameters.

Optimal power flow solution using water cycle algorithm

Optimal power flow (OPF) is known as one of the most important planning and scheduling tools in electrical power systems. The OPF problem is a non-convex, NP-hard optimization problem, therefore, the applications of metaheuristic algorithms in the OPF problem have been gained more attentions in recent years. This article investigates successful application of recently developed optimizer, so called, water cycle algorithm (WCA) on the OPF. The WCA, as a metaheuristic optimization method, is inspired by water cycle process in nature. The IEEE 57-bus test system has been taken into account. The obtained optimization results show the better optimal power flow solution using the WCA compared with the other reported optimization approaches. Therefore, the applied WCA can be considered as an alternative approach for tackling OPF.

Optimization of hydropower storage projects using harmony search algorithm

This paper proposes a harmony search algorithm-based optimization of design and operation of hydropower storage systems. The optimization formulation of the problem is a nonconvex nonlinear program difficult to solve by gradient-based nonlinear programming techniques. The search space of the problem is large due to number of operational variables. Harmony search optimization algorithm is applied in the problem of design and operation optimization of Bakhtiari Dam and its powerplant project in Iran. The problem is solved in two cases of optimizing only design variables and optimizing design and operational variables, simultaneously, and the promising results obtained are presented.

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.