Search Results - (( evolution optimisation based algorithm ) OR ( using evolutionary bat algorithm ))

Reservoir operation based on evolutionary algorithms and multi-criteria decision-making under climate change and uncertainty by Ehteram, Mohammad, Mousavi, Sayed Farhad, Karami, Hojat, Farzin, Saeed, Singh, Vijay P., Chau, Kwok Wing, El-Shafie, Ahmed

“…Results showed the volume of water to be released for the future period, based on all evolutionary algorithms used, was less than for the base period, and the bat algorithm with high-reliability index and low vulnerability index performed better among other evolutionary algorithms.…”
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Gravitational search – bat algorithm for solving single and bi-objective of non-linear functions by Abbas, Iraq Tareq

“…The use of the BOGSA algorithm aims to create a new equation for the calculation of the masses of population individuals, as found in the theoretical work in the Strength Pareto Evolutionary Algorithm two (SPEAII) algorithm. …”
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Enhancing benchmark optimization with evolutionary random approach: A comparative analysis of modified adaptive bats sonar algorithm (MABSA) by Nor Shuhada, Ibrahim, Nafrizuan, Mat Yahya, Saiful Bahri, Mohamed, Mohd Ismail, Yusof

“…This article presents a novel hybrid algorithm, combining the Modified Adaptive Bats Sonar Algorithm (MABSA) with the Squirrel Search Algorithm (SSA), and compares its performance with the original MABSA. …”
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A hybrid bat–swarm algorithm for optimizing dam and reservoir operation by Yaseen, Zaher Mundher, Allawi, Mohammed Falah, Karami, Hojat, Ehteram, Mohammad, Farzin, Saeed, Ahmed, Ali Najah, Koting, Suhana, Mohd, Nuruol Syuhadaa, Jaafar, Wan Zurina Wan, Afan, Haitham Abdulmohsin, El-Shafie, Ahmed

“…Recently, evolutionary algorithms (EA) are used to develop optimal operation rules for dam and reservoir water systems. …”
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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.

A hybrid bat�swarm algorithm for optimizing dam and reservoir operation by Yaseen Z.M., Allawi M.F., Karami H., Ehteram M., Farzin S., Ahmed A.N., Koting S.B., Mohd N.S., Jaafar W.Z.B., Afan H.A., El-Shafie A.

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 Novel Hybrid Evolutionary Data-Intelligence Algorithm for Irrigation and Power Production Management: Application to Multi-Purpose Reservoir Systems by Yaseen, Zaher, Ehteram, Mohammad, Hossain, Md., Fai, Chow, Koting, Suhana, Mohd, Nuruol Syuhadaa, Jaafar, Wan Zurina Wan, Afan, Haitham Abdulmohsin, Lai, Sai Hin, Zaini, Nuratiah, Ahmed, Ali, El-Shafie, Ahmed

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Performance Analyses of Nature-inspired Algorithms on the Traveling Salesman’s Problems for Strategic Management by Julius, Beneoluchi Odili, M. N. M., Kahar, Noraziah, Ahmad, M., Zarina, Riaz, Ul Haq

“…After critical assessments of the performances of eleven algorithms consisting of two heuristics (Randomized Insertion Algorithm and the Honey Bee Mating Optimization for the Travelling Salesman’s Problem), two trajectory algorithms (Simulated Annealing and Evolutionary Simulated Annealing) and seven population-based optimization algorithms (Genetic Algorithm, Artificial Bee Colony, African Buffalo Optimization, Bat Algorithm, Particle Swarm Optimization, Ant Colony Optimization and Firefly Algorithm) in solving the 60 popular and complex benchmark symmetric Travelling Salesman’s optimization problems out of the total 118 as well as all the 18 asymmetric Travelling Salesman’s Problems test cases available in TSPLIB91. …”
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A novel hybrid evolutionary data-intelligence algorithm for irrigation and power production management: Application to multi-purpose reservoir systems by Yaseen Z.M., Ehteram M., Hossain M.S., Fai C.M., Koting S.B., Mohd N.S., Jaafar W.Z.B., Afan H.A., Hin L.S., Zaini N., Ahmed A.N., El-Shafie A.

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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Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm by Mohamadi S., Sammen S.S., Panahi F., Ehteram M., Kisi O., Mosavi A., Ahmed A.N., El-Shafie A., Al-Ansari N.

Multi-Objective Optimal Energy Management of Nanogrid Using Improved Pelican Optimization Algorithm by Jamal S., Pasupuleti J., Rahmat N.A., Tan N.M.L.

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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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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Cloud Worm Detection and Response Technique By Integrating The Enhanced Genetic Algorithm An Threat Level by Hasan Mahmoud Sha'ban Kanaker

iBUST: An intelligent behavioural trust model for securing industrial cyber-physical systems by Azad, Saiful, Mahmud, Mufti, Kamal Zuhairi, Zamli, Kaiser, M. Shamim, Jahan, Sobhana, Razzaque, Md Abdur

“…In addition, a new optimisation model for finding optimum parameter values in the MEDF and an algorithm for transmuting a 1D quantitative feature into a respective categorical feature are developed to facilitate the model. …”
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