WCA: A weighting local search for constrained combinatorial test optimization

Fu, Yingjie; Lei, Zhendong; Lin, Jinkun; Wang, Haoran

doi:10.1016/j.infsof.2020.106288

Cited by 13 publications

(16 citation statements)

References 20 publications

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“…ey used the HC algorithm to address two MOs: the ECA (equal cluster size approach) and the MCA (maximize cluster approach). Later, Barros et al [19] used a new objective to assess the efficacy of the ECA and MCA formulations. eir empirical analysis revealed that, like with MCA and ECA, equivalent results could be achieved with fewer objectives.…”

Section: Related Workmentioning

confidence: 99%

“…A modular structure with roughly equal-sized clusters is produced in ECA, which helps in cluster disordering. It prevents large clusters and isolated clusters [9,19]. Only one objective is different between MCA and MCA: the number of modules in a cluster.…”

Section: Roughly Equal Size Cluster Approach (Resca)mentioning

confidence: 99%

“…SM is an NP-Hard problem and has been solved in the past through search-based optimization. e search-based approach, proposed by Hwa et al [16], enhances cohesion and coupling [17,18], and improves the software structure by enhancing the criteria for coupling and cohesion [9,18,19]. Moreover, meta-heuristic approaches [17,20] are also used to solve modularization problems such as Barros [21] investigated the software clustering problem's efficiency and efficacy of using two composite objectives.…”

Section: Introductionmentioning

confidence: 99%

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An Enhanced Multifactor Multiobjective Approach for Software Modularization

Khan

Naseem

Anwar

et al. 2022

Mathematical Problems in Engineering

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Complex software systems, meant to facilitate organizations, undergo frequent upgrades that can erode the system architectures. Such erosion makes understandability and maintenance a challenging task. To this end, software modularization provides an architectural-level view that helps to understand system architecture from its source code. For modularization, nondeterministic search-based optimization uses single-factor single-objective, multifactor single-objective, and single-factor multiobjective, which have been shown to outperform deterministic approaches. The proposed MFMO approach, which uses both a heuristic (Hill Climbing and Genetic) and a meta-heuristic (nondominated sorting genetic algorithms NSGA-II and III), was evaluated using five data sets of different sizes and complexity. In comparison to leading software modularization techniques, the results show an improvement of 4.13% in Move and Join operations (MoJo, MoJoFM, and NED).

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Section: Related Workmentioning

confidence: 99%

Section: Roughly Equal Size Cluster Approach (Resca)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Enhanced Multifactor Multiobjective Approach for Software Modularization

Khan

Naseem

Anwar

et al. 2022

Mathematical Problems in Engineering

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“…There are available options discussed in the literature to improve the effectiveness of these mechanisms. Random walks [20] can be alternatively employed to diversify the search space to some extent whereas local search methods [21,22] are exemplified as popular strategies for the exploitation which allows for intensification on the fertile areas obtained through the iterative process. Creating a synergy between two or more metaheuristic algorithms [23,24] can also improve the solution accuracy, but this kind of hybridization burdens a significant amount of computational cost which entails evident problems in ill-defined expensive optimization problems.…”

Section: Introductionmentioning

confidence: 99%

A novel chaotic manta-ray foraging optimization algorithm for thermo-economic design optimization of an air-fin cooler

Turgut

2020

SN Appl. Sci.

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This research study aims to introduce chaos theory into the Manta Ray Foraging Optimization (MRFO) Algorithm and optimize a real-world design problem through the chaos-enhanced versions of this method. Manta Ray Foraging Optimization algorithm is a bio-inspired swarm intelligence-based metaheuristic algorithm simulating the distinctive food search behaviors of the manta rays. However, MRFO suffers from some intrinsic algorithmic inefficiencies such as slow and premature convergence and unexpected entrapment to the local optimum points in the search domain like most of the metaheuristic algorithms in the literature. Recently, random numbers generated by chaos theory have been incorporated into the metaheuristic algorithms to solve these problems. More than twenty chaotic maps are applied to the base algorithm and ten best performing methods are considered for performance evaluation on high-dimensional optimization test problems. Forty test problems comprising unimodal and multimodal functions have been solved by chaotic variants of MRFO and extensive statistical analysis is performed. Furthermore, thermo-economic design optimization of an air-fin cooler is maintained by the chaotic MRFO variants to assess their optimization capabilities over complex engineering design problems. Ten decisive design variables of an air fin cooler are optimized in terms of total annual cost rates and optimum solutions obtained by five best chaotic MRFO algorithms are compared to the preliminary design. A significant improvement is observed in the objective function values when MRFO with chaotic operators is applied to this considered thermal design problem.

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“…This paper is devoted to developing an efficient SLS algorithm for solving HRS and URS instances. The improvement of weighting schemes has become the mainstream of optimizing SLS algorithms [51][52][53]. In this work, we propose two ideas about clause weighting schemes.…”

Section: Introductionmentioning

confidence: 99%

More efficient stochastic local search for satisfiability

Wu²,

et al. 2020

Appl Intell

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Uniform random satisfiability (URS) and hard random satisfiability (HRS) are two significant generalizations of random satisfiability (RS). Recently, great breakthroughs have been made on stochastic local search (SLS) algorithms for uniform RS, resulting in several state-of-the-art algorithms, e.g., Dimetheus, YalSAT, ProbSAT and Score2SAT. However, compared to the great progress of SLS on URS, the performance of SLS on HRS lags far behind. In this paper, we propose two global clause weighting schemes and a new hybrid scoring function called SA based on a linear combination of a property score and property age, and then apply a second-level-biased random walk strategy based on two clause weighting schemes and SA to develop a new SLS solver called BRSAP. To evaluate the performance of BRSAP, we conduct extensive experiments to compare BRSAP with state-of-the-art SLS solvers and complete solvers on HRS instances and URS instances from SAT Competition 2017 and SAT Competition 2018 as well as 4100 generated satisfiable large HRS and URS ones. The experiments illustrate that BRSAP obviously outperforms its competitors, indicating the effectiveness of BRSAP. We also analyze the effectiveness of the underlying ideas in BRSAP.

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WCA: A weighting local search for constrained combinatorial test optimization

Cited by 13 publications

References 20 publications

An Enhanced Multifactor Multiobjective Approach for Software Modularization

An Enhanced Multifactor Multiobjective Approach for Software Modularization

A novel chaotic manta-ray foraging optimization algorithm for thermo-economic design optimization of an air-fin cooler

More efficient stochastic local search for satisfiability

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