Comparative Analysis of  Metaheuristic Approaches for Makespan Minimization for No Wait Flow Shop Scheduling Problem

Bewoor, Laxmi A.; Prakash, V. Chandra; Sapkal, Sagar U.

doi:10.11591/ijece.v7i1.pp417-423

Cited by 7 publications

(7 citation statements)

References 17 publications

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“…Initially, and as explained in [16,17], the first step is to create a random initial solution and then perform additional operations on it with the objective of satisfying the hard constraints thereby arriving at the correct initial solution [18]. In fact, this has been a rather time-consuming method.…”

Section: The Initial State Phase (Isp)mentioning

confidence: 99%

“…In the former class of algorithms large memory sizes are required [14,15], while in the latter category certain disadvantages are commonly encountered. These deal with the fact that the optimal solution usually depends on the initial state where the speed of convergence is rather too slow than many other algorithms [16,17]. Furthermore, in this work no consideration will be given to Tabu's search algorithm which is classified under the local search family and would require a large number of iterations for its convergence [18].…”

Section: Introductionmentioning

confidence: 99%

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Courses timetabling based on hill climbing algorithm

Rjoub

2020

IJECE

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In addition to its monotonous nature and excessive time requirements, the manual school timetable scheduling often leads to more than one class being assigned to the same instructor, or more than one instructor being assigned to the same classroom during the same slot time, or even leads to exercise in intentional partialities in favor of a particular group of instructors. In this paper, an automated school timetable scheduling is presented to help overcome the traditional conflicts inherent in the manual scheduling approach. In this approach, hill climbing algorithms have been modified to transact hard and soft constraints. Soft constraints are not easy to be satisfied typically, but hard constraints are obligated. The implementation of this technique has been successfully experimented in different schools with various kinds of side constraints. Results show that the initial solution can be improved by 72% towards the optimal solution within the first 5 seconds and by 50% from the second iteration while the optimal solution will be achieved after 15 iterations ensuring that more than 50% of scientific courses will take place in the early slots time while more than 50% of non-scientific courses will take place during the later time's slots.

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Section: The Initial State Phase (Isp)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Courses timetabling based on hill climbing algorithm

Rjoub

2020

IJECE

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“…This optimisation method aims to find the parameters that give the minimum (or maximum) of an objective function [17]. We opt to use the PSO algorithm in this work due to the fact that it converges more to the optimal solution with less overhead of parameter setting and less computation time in comparaison with other metaheuristic methods [18].…”

Section: Particle Swarm Optimisation Technoque (Pso)mentioning

confidence: 99%

Optimal location and reactive power injection of wind farms and SVC’s units using voltage indices and PSO

Cherkaoui¹,

Belfqih²,

Mariami³

et al. 2019

IJECE

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<p class="Default">Nowadays, the use of the wind energy has known an important increase because it is clean and cheap. However, many technical issues could occur due to the integration of wind power plants into power grids. As a result, many countries have published grid code requirements that new installed wind turbines have to satisfy in order to facilitate its intergration to electrical networks. Among those requirements, the wind farms must be able to participate to ancillary services for instance voltage regulation and reactive power control. Nevertheless, in case of small wind farms having not the necessary reactive power capability to contribute to reactive power support, Flexible AC Transmission Systems (FACTS) devices could also be used to participate to reactive power support. In this paper, an optimization method based on particle swarm optimization (PSO) technique is presented. This method allows getting the optimal location and reactive power injection of both wind power plants (WPP) and synchronous var compensators (SVC) with the objective to improve the voltage profile and to minimize the active power losses. The IEEE 14 bus system and a 20 MW wind farm based doubly fed induction generator (DFIG) are used to validate the proposed algorithm. The simulation results are analysed and compared.</p>

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“…16 Ahmed et al [27] To generate constraints test cases in combinatorial testing MOPSO Produced impressive results with binomial time for generation. 17 Sheng et al [28] To generate test cases for handling constraints in Combinatorial Interaction Testing (CIT)…”

Section: Robdd Graph and Pso Algorithmmentioning

confidence: 99%

A Critical Review on Automated Test Case Generation for Conducting Combinatorial Testing Using Particle Swarm Optimization

Prakash¹,

Tatale²,

Kondhalkar³

et al. 2018

IJET

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In software development life cycle, testing plays the significant role to verify requirement specification, analysis, design, coding and to estimate the reliability of software system. A test manager can write a set of test cases manually for the smaller software systems. However, for the extensive software system, normally the size of test suite is large, and the test suite is prone to an error committed like omissions of important test cases, duplication of some test cases and contradicting test cases etc. When test cases are generated automatically by a tool in an intelligent way, test errors can be eliminated. In addition, it is even possible to reduce the size of test suite and thereby to decrease the cost & time of software testing. It is a challenging job to reduce test suite size. When there are interacting inputs of Software under Test (SUT), combinatorial testing is highly essential to ensure higher reliability from 72 % to 91 % or even more than that. A meta-heuristic algorithm like Particle Swarm Optimization (PSO) solves optimization problem of automated combinatorial test case generation. Many authors have contributed in the field of combinatorial test case generation using PSO algorithms. We have reviewed some important research papers on automated test case generation for combinatorial testing using PSO. This paper provides a critical review of use of PSO and its variants for solving the classical optimization problem of automatic test case generation for conducting combinatorial testing.

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Comparative Analysis of Metaheuristic Approaches for Makespan Minimization for No Wait Flow Shop Scheduling Problem

Cited by 7 publications

References 17 publications

Courses timetabling based on hill climbing algorithm

Courses timetabling based on hill climbing algorithm

Optimal location and reactive power injection of wind farms and SVC’s units using voltage indices and PSO

A Critical Review on Automated Test Case Generation for Conducting Combinatorial Testing Using Particle Swarm Optimization

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