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  • Open Access


    Maintain Optimal Configurations for Large Configurable Systems Using Multi-Objective Optimization

    Muhammad Abid Jamil1,*, Deafallah Alsadie1, Mohamed K. Nour1, Normi Sham Awang Abu Bakar2

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4407-4422, 2022, DOI:10.32604/cmc.2022.029096

    Abstract To improve the maintenance and quality of software product lines, efficient configurations techniques have been proposed. Nevertheless, due to the complexity of derived and configured products in a product line, the configuration process of the software product line (SPL) becomes time-consuming and costly. Each product line consists of a various number of feature models that need to be tested. The different approaches have been presented by Search-based software engineering (SBSE) to resolve the software engineering issues into computational solutions using some metaheuristic approach. Hence, multiobjective evolutionary algorithms help to optimize the configuration process of SPL. In this paper, different multi-objective… More >

  • Open Access


    Minimizing Total Tardiness in a Two-Machine Flowshop Scheduling Problem with Availability Constraints

    Mohamed Ali Rakrouki1,2,*, Abeer Aljohani1, Nawaf Alharbe1, Abdelaziz Berrais2, Talel Ladhari2

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1119-1134, 2023, DOI:10.32604/iasc.2023.028604

    Abstract In this paper, we consider the problem of minimizing the total tardiness in a deterministic two-machine permutation flowshop scheduling problem subject to release dates of jobs and known unavailability periods of machines. The theoretical and practical importance of minimizing tardiness in flowshop scheduling environment has motivated us to investigate and solve this interested two-machine scheduling problem. Methods that solve this important optimality criterion in flowshop environment are mainly heuristics. In fact, despite the -hardness in the strong sense of the studied problem, to the best of our knowledge there are no approximate algorithms (constructive heuristics or metaheuristics) or an algorithm… More >

  • Open Access


    Managing Software Testing Technical Debt Using Evolutionary Algorithms

    Muhammad Abid Jamil*, Mohamed K. Nour

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 735-747, 2022, DOI:10.32604/cmc.2022.028386

    Abstract Technical debt (TD) happens when project teams carry out technical decisions in favor of a short-term goal(s) in their projects, whether deliberately or unknowingly. TD must be properly managed to guarantee that its negative implications do not outweigh its advantages. A lot of research has been conducted to show that TD has evolved into a common problem with considerable financial burden. Test technical debt is the technical debt aspect of testing (or test debt). Test debt is a relatively new concept that has piqued the curiosity of the software industry in recent years. In this article, we assume that the… More >

  • Open Access


    Differential Evolution Algorithm with Hierarchical Fair Competition Model

    Amit Ramesh Khaparde1,*, Fawaz Alassery2, Arvind Kumar3, Youseef Alotaibi4, Osamah Ibrahim Khalaf5, Sofia Pillai6, Saleh Alghamdi7

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1045-1062, 2022, DOI:10.32604/iasc.2022.023270

    Abstract This paper presents the study of differential evolution algorithm with hierarchical fair competition model (HFC-DE). HFC model is based on the fair competition of societal system found in natural world. In this model, the population is split into hierarchy and the competition is allowed between the hierarchical members. During evolution, the population members are allowed to move within the hierarchy levels. The standard differential evolution algorithm is used for population evolution. Experimentation has carried out to define the parameter for proposed model on test suit having unimodal problems and multi-model problems. After analyzing the results, the two variants of HFC-DE… More >

  • Open Access


    Optimization of Reliability–Redundancy Allocation Problems: A Review of the Evolutionary Algorithms

    Haykel Marouani1,2, Omar Al-mutiri1,*

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 537-571, 2022, DOI:10.32604/cmc.2022.020098

    Abstract The study of optimization methods for reliability–redundancy allocation problems is a constantly changing field. New algorithms are continually being designed on the basis of observations of nature, wildlife, and humanity. In this paper, we review eight major evolutionary algorithms that emulate the behavior of civilization, ants, bees, fishes, and birds (i.e., genetic algorithms, bee colony optimization, simulated annealing, particle swarm optimization, biogeography-based optimization, artificial immune system optimization, cuckoo algorithm and imperialist competitive algorithm). We evaluate the mathematical formulations and pseudo-codes of each algorithm and discuss how these apply to reliability–redundancy allocation problems. Results from a literature survey show the best… More >

  • Open Access


    Optimal Path Planning for Intelligent UAVs Using Graph Convolution Networks

    Akshya Jothi, P. L. K. Priyadarsini*

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1577-1591, 2022, DOI:10.32604/iasc.2022.020974

    Abstract Unmanned Aerial Vehicles (UAVs) are in use for surveillance services in the geographic areas, that are very hard and sometimes not reachable by humans. Nowadays, UAVs are being used as substitutions to manned operations in various applications. The intensive utilization of autonomous UAVs has given rise to many new challenges. One of the vital problems that arise while deploying UAVs in surveillance applications is the Coverage Path Planning(CPP) problem. Given a geographic area, the problem is to find an optimal path/tour for the UAV such that it covers the entire area of interest with minimal tour length. A graph can… More >

  • Open Access


    Effectiveness Assessment of the Search-Based Statistical Structural Testing

    Yang Shi*, Xiaoyu Song, Marek Perkowski, Fu Li

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2191-2207, 2022, DOI:10.32604/cmc.2022.018718

    Abstract Search-based statistical structural testing (SBSST) is a promising technique that uses automated search to construct input distributions for statistical structural testing. It has been proved that a simple search algorithm, for example, the hill-climber is able to optimize an input distribution. However, due to the noisy fitness estimation of the minimum triggering probability among all cover elements (Tri-Low-Bound), the existing approach does not show a satisfactory efficiency. Constructing input distributions to satisfy the Tri-Low-Bound criterion requires an extensive computation time. Tri-Low-Bound is considered a strong criterion, and it is demonstrated to sustain a high fault-detecting ability. This article tries to… More >

  • Open Access


    Selection and Optimization of Software Development Life Cycles Using a Genetic Algorithm

    Fatimah O. Albalawi, Mashael S. Maashi*

    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 39-52, 2021, DOI:10.32604/iasc.2021.015657

    Abstract In the software field, a large number of projects fail, and billions of dollars are spent on these failed projects. Many software projects are also produced with poor quality or they do not exactly meet customers’ expectations. Moreover, these projects may exceed project budget and/or time. The complexity of managing software development projects and the poor selection of software development life cycle (SDLC) models are among the top reasons for such failure. Various SDLC models are available, but no model is considered the best or worst. In this work, we propose a new methodology that solves the SDLC optimization problem… More >

  • Open Access


    Feasibility-Guided Constraint-Handling Techniques for Engineering Optimization Problems

    Muhammad Asif Jan1,*, Yasir Mahmood1, Hidayat Ullah Khan2, Wali Khan Mashwani1, Muhammad Irfan Uddin3, Marwan Mahmoud4, Rashida Adeeb Khanum5, Ikramullah6, Noor Mast3

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 2845-2862, 2021, DOI:10.32604/cmc.2021.015294

    Abstract The particle swarm optimization (PSO) algorithm is an established nature-inspired population-based meta-heuristic that replicates the synchronizing movements of birds and fish. PSO is essentially an unconstrained algorithm and requires constraint handling techniques (CHTs) to solve constrained optimization problems (COPs). For this purpose, we integrate two CHTs, the superiority of feasibility (SF) and the violation constraint-handling (VCH), with a PSO. These CHTs distinguish feasible solutions from infeasible ones. Moreover, in SF, the selection of infeasible solutions is based on their degree of constraint violations, whereas in VCH, the number of constraint violations by an infeasible solution is of more importance. Therefore,… More >

  • Open Access


    Mining of Data from Evolutionary Algorithms for Improving Design Optimization

    Y.S. Lian1, M.S. Liou2

    CMES-Computer Modeling in Engineering & Sciences, Vol.8, No.1, pp. 61-72, 2005, DOI:10.3970/cmes.2005.008.061

    Abstract This paper focuses on integration of computational methods for design optimization based on data mining and knowledge discovery. We propose to use radial basis function neural networks to analyze the large database generated from evolutionary algorithms and to extract the cause-effect relationship, between the objective functions and the input design variables. The aim is to improve the optimization process by either reducing the computation cost or improving the optimal. Also, it is hoped to provide designers with the salient design pattern about the problem under consideration, from the physics-based simulations. The proposed technique is applied to both academic problems and… More >

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