Search Results - (( software optimization _ algorithm ) OR ( evolution optimisation based algorithm ))

Optimal placement, sizing and power factor of distributed generation: A comprehensive study spanning from the planning stage to the operation stage by Huy P.D., Ramachandaramurthy V.K., Yong J.Y., Tan K.M., Ekanayake J.B.

Performance evaluation and benchmarking of an extended computational model of ant colony system for DNA sequence design by Zuwairie, Ibrahim, Mohd Falfazli, Mat Jusof, Mohd Zaidi, Mohd Tumari

“…Ant colony system (ACS) algorithm is one of the biologically inspired algorithms that have been introduced to effectively solve a variety of combinatorial optimisation problems. …”
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A competitive co-evolutionary approach for the nurse scheduling problem by Mohamad Nazri, Maizatul Farhana, Mohd Yusoh, Zeratul Izzah, Basiron, Halizah, Daud, Azlina

“…The competitive approach further exhibits smoother convergence behaviour across generations, indicating stronger optimisation dynamics and improved robustness. These findings demonstrate that competitive co-evolution provides an effective and practical alternative to static fitness-based evolutionary methods for nurse scheduling, with broader applicability to healthcare scheduling and constraint optimisation problems.…”
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A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, Fakhrud Din, Shah Khalid, Kamal Zuhairi Zamli, Aftab Alam

“…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, ., Fakhrud, Din, Shah, Khalid, Kamal Z., Zamli, Alam, Aftab

“…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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Stock market turning points rule-based prediction / Lersak Photong … [et al.] by Photong, Lersak, Sukprasert, Anupong, Boonlua, Sutana, Ampant, Pravi

“…Finally, rule-based optimisation techniques such as Particle Swarm Optimization (PSO), Differential Evolution (DE) and Grey Wolf Optimizer (GWO) were used to minimise the amount of time employed in the stock market turning points prediction. …”
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An adaptive flower pollination algorithm for minimizing software testing redundancy by M. N., Kabir, Ali, Jahan, Alsewari, Abdulrahman A., Kamal Z., Zamli

“…In this paper, we present an adaptive flower pollination algorithm for solving an optimization problem, i.e., minimization of software testing redundancy. …”
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A comparative study on testing optimization techniques with combinatorial interaction testing for optimizing software product line testing by Maidin, Nur Farrahin, Hassan, Sa’Adah, Baharom, Salmi, Md. Sultan, Abu Bakar

“…An experiment has been conducted on the most commonly used optimization algorithms in previous studies. The optimization algorithms we explored are the Genetic Algorithm, Cuckoo Search algorithm, Ant Colony algorithm, and Particle Swarm Optimization algorithm. …”
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Development of New Genetic Algorithm Software for Blow Mould Process by K., Kadirgama, M. M., Noor, R., Daud, M. R. M., Rejab

“…The approach is based on newly development of Genetic Algorithm software. In this work, the objectives were to optimized parameters with newly develop software and compare with statistical software. …”
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Towards Software Product Lines Optimization Using Evolutionary Algorithms by Jamil, Muhammad Abid, K Nour, Mohamed, Alhindi, Ahmed Hasan, Awang Abu Bakar, Normi Sham, Arif, Muhammad, Muhammad Aljabri, Tareq

“…This research provides a framework to compare the performance of different multi-objective Evolutionary Algorithms in software product line context. We report on the problem encoding, variation operators and different types of algorithms: Indicator Based Evolutionary Algorithm (IBEA), Non-Dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Evolutionary Algorithms based on Decomposition (MOEA/D) and Strength Pareto Evolutionary algorithm II (SPEA-II). …”
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Particle swarm optimization (PSO) for CNC route problem by Nur Azia Azwani, Ismail

“…We often see many of the method of Genetic Algorithm (GA), Ant Colony Optimization (ACO), Simulated Annealing Algorithm (SAA) and PSO are used for any optimization problems. …”
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Maintain optimal configurations for large configurable systems using multi-objective optimization by Jamil, Muhammad Abid, Alsadie, Deafallah, Nour, Mohamed K., Awang Abu Bakar, Normi Sham

“…Hence, multiobjective evolutionary algorithms help to optimize the configuration process of SPL. …”
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African Buffalo Optimization by Odili, Julius Beneoluchi, M. N. M., Kahar

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Multiobjective evolutionary algorithms NSGA-II and NSGA-III for software product lines testing optimization by Jamil, Muhammad Abid, Alhindi, Ahmad, Arif, Muhammad, Nour, Mohamed K, Awang Abu Bakar, Normi Sham, Aljabri, Tareq Fahad

“…Hence, the testing process of SPL can utilize a multi-objective optimization algorithm to optimize the testing process. …”
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Software defect prediction framework based on hybrid metaheuristic optimization methods by Wahono, Romi Satria

“…The classification algorithm is a popular machine learning approach for software defect prediction. …”
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Successor selection for Ant Colony Optimization technique algorithm / Muhammad Iskandar Isman by Isman, Muhammad Iskandar

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A kidney algorithm with elitism for combinatorial testing problem by Bahomaid, Ameen A., Alsewari, Abdulrahman A., Kamal Z., Zamli, Alhendawi, Kamal M., Al-Janabi, Ala Aldeen

“…Kidney algorithm (KA) is a recent computational AI algorithm with sufficient optimization capability which outperforms the other AI algorithms (such as Genetic Algorithm (GA), Cuckoo Search (CS), Particle Swarm Optimization (PSO), Harmony Search (HS)) from some aspects. …”
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B-spline curve fitting with different parameterization methods by Kheng, Jia Shen

“…After generating control points, distance between the generated and original data points is used to identify the error of the algorithm. Later, genetic algorithm and differential evolution optimization are used to optimise the error of the curve. …”
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Optimization of mycelium growth using genetic algorithm for multi-objective functions by Muhamad Faiz, Abu Bakar

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Reliability assessment of power system generation adequacy with wind power using population-based intelligent search methods by Kadhem, Athraa Ali

“…This study sought to examine the performance of three newly proposed techniques, for reliability assessment of the power systems, namely Disparity Evolution Genetic Algorithm (DEGA), Binary Particle Swarm Optimisation (BPSO), and Differential Evolution Optimization Algorithm (DEOA). …”
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