Comparative Analysis of Metaheuristic Approaches for m-Machine No Wait Flow Shop Scheduling for minimizing Total Flow Time with Stochastic Input

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

doi:10.21817/ijet/2016/v8i6/160806265

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…Bewoor et al [29] compared the performance of PSO with Genetic Algorithm (GA) and Tabu Search (TS) and proved PSO is providing better solution as compared to other metaheuristic for stochastic input. Bewoor et al [25] further developed Proposed Hybrid PSO (PHPSO) algorithm for solving combinatorial optimization problem of No Wait Flow Shop Scheduling (NWFSSP) and proved that PSO superior than other algorithms available in the literature. Inspired from the idea of applying PSO for NWFSSP this paper is an attempt for reviewing application of PSO for test case generation and following section provides a critical review on the same.…”

Section: Literature Reviewmentioning

confidence: 99%

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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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Section: Literature Reviewmentioning

confidence: 99%

“…It reduces testing cost by selecting appropriate test cases. 15 Sahin et al [25] To generate combinatorial test cases Metaheuristic Algorithm For easy problems meta-heuristics produce comparable and acceptable results. For multimodality problems and a number of constraints ABC proves to be superior.…”

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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“…The comparative analysis of various metaheuristics for NWFSSP, by Bewoor et al [29,30], advocated the effectiveness of PSO. Some remarkable metaheuristics were developed by Pan et al [9,10] for solving NWFSSP.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Hybrid Particle Swarm Optimization Algorithm for Solving NP-Hard No-Wait Flow Shop Scheduling Problems

Bewoor

Prakash

Sapkal³

2017

Algorithms

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Abstract:The no-wait flow shop is a flowshop in which the scheduling of jobs is continuous and simultaneous through all machines without waiting for any consecutive machines. The scheduling of a no-wait flow shop requires finding an appropriate sequence of jobs for scheduling, which in turn reduces total processing time. The classical brute force method for finding the probabilities of scheduling for improving the utilization of resources may become trapped in local optima, and this problem can hence be observed as a typical NP-hard combinatorial optimization problem that requires finding a near optimal solution with heuristic and metaheuristic techniques. This paper proposes an effective hybrid Particle Swarm Optimization (PSO) metaheuristic algorithm for solving no-wait flow shop scheduling problems with the objective of minimizing the total flow time of jobs. This Proposed Hybrid Particle Swarm Optimization (PHPSO) algorithm presents a solution by the random key representation rule for converting the continuous position information values of particles to a discrete job permutation. The proposed algorithm initializes population efficiently with the Nawaz-Enscore-Ham (NEH) heuristic technique and uses an evolutionary search guided by the mechanism of PSO, as well as simulated annealing based on a local neighborhood search to avoid getting stuck in local optima and to provide the appropriate balance of global exploration and local exploitation. Extensive computational experiments are carried out based on Taillard's benchmark suite. Computational results and comparisons with existing metaheuristics show that the PHPSO algorithm outperforms the existing methods in terms of quality search and robustness for the problem considered. The improvement in solution quality is confirmed by statistical tests of significance.

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The Analysis of the Influence of Damaged Concrete Compression Zone on the RC Beam Using FEM

Klym,

Blikharskyy,

Panchenko

et al. 2023

Lecture Notes in Civil Engineering

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Comparative Analysis of Metaheuristic Approaches for m-Machine No Wait Flow Shop Scheduling for minimizing Total Flow Time with Stochastic Input

Cited by 4 publications

References 8 publications

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

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

Evolutionary Hybrid Particle Swarm Optimization Algorithm for Solving NP-Hard No-Wait Flow Shop Scheduling Problems

The Analysis of the Influence of Damaged Concrete Compression Zone on the RC Beam Using FEM

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