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Öğe Analysis of ımproved evolutionary algorithms using students' datasets(IEEE, 2022) Ajibade, Samuel-Soma M.; Ayaz, Muhammad; Ngo-Hoang, Dai-Long; Tabuena, Almighty C.; Rabbi, Fazle; Tilaye, Getahun Fikadu; Bassey, Mbiatke AnthonyEvolutionary Algorithms (EAs) are powerful heuristic search approaches which relies on Darwinian evolution that capture global solutions to complex optimization problems which has powerful features of reliability and versatility. (EAs) such as Particle swarm optimization (PSO) is a global optimization method that is extremely effective. PSO's flaws include slow convergence, premature convergence, and getting stuck at local optima. In this paper, chaotic map and dynamic-weight Particle Swarm Optimization (CHDPSOA) are combined with PSO to enhance the search strategy through adjusting the inertia weight of PSO and changing the position update formula in the (CHDPSOA), resulting in efficient balancing for local and global PSO feature selection processes. The performance of CHDPSOA was compared to that of three metaheuristic techniques: Differential Evolution (DE) and the original PSO, using eight numerical functions. The validation of this technique is carried out on four different datasets. The results show that the CHDPSOA is a good feature selection technique that balances the exploration and exploitation search processes to produce good results. The proposed CHDPSOA method performed well in correctly categorizing features using the KNN Classifier for all four datasets.Öğe Feature Selection for Metaheuristics Optimization Technique with Chaos(Institute of Electrical and Electronics Engineers Inc., 2022) Chaudhury, Sushovan; Oyebode, Oluwadare Joshua; Ngo Hoang, Dai-Long; Rabbi, Fazle; Ajibade, Samuel-Soma M.Particle swarm optimization (PSO) is a global optimization method that is extremely effective. PSO's flaws include slow convergence, premature convergence, and getting stuck at local optima. In this paper, the chaos map and dynamic-weight Particle Swarm Optimization (CPSO) are combined with PSO to improve the search process by adjusting the inertia weight of PSO and changing the position update formula in the Chaos dynamic-weight Particle Swarm Optimization (CPSO), resulting in efficient balancing for local and global PSO feature selection processes. Using eight numerical functions, the performance of CPSO was compared to that of two metaheuristic techniques which are the original PSO and Differential Evolution (DE). The results reveal that the CPSO is an efficient feature selection technique that generates good results by balancing the exploration and exploitation search processes.Öğe Gaussian map to improve firefly algorithm performance(Institute of Electrical and Electronics Engineers Inc., 2022) Rabbi, Fazle; Ayaz, Muhammad; Dayupay, Johnry P.; Oyebode, Oluwadare Joshua; Gido, Nathaniel G.; Adhikari, Nirmal; Tabuena, Almighty C.; Ajibade, Samuel-Soma M.; Bassey, Mbiatke AnthonyFirefly Algorithm (FA) mimics firefly behavior by flashing and attracts them. Firefly's global search mobility is improved for dependable global optimization using chaotic maps in this work. Investigations of benchmark problems with chaotic maps are carried out in depth. The system uses eight separate chaotic maps to fine-tune the firefly's enticing movements. By using planned chaotic transmissions instead of fixed values, the new method beats classic firefly methods. According to statistical data and the success rates of FA, the new algorithms improve the solution's performance and the reliability of global optimality.Öğe New Insights into the Emerging Trends Research of Machine and Deep Learning Applications in Energy Storage: A Bibliometric Analysis and Publication Trends(Econjournals, 2023) Ajibade, Samuel-Soma M.; Zaidi, Abdelhamid; Al Luhayb, Asamh Saleh M.; Adediran, Anthonia Oluwatosin; Voumik, Liton Chandra; Rabbi, FazleThe publication trends and bibliometric analysis of the research landscape on the applications of machine and deep learning in energy storage (MDLES) research were examined in this study based on published documents in the Elsevier Scopus database between 2012 and 2022. The PRISMA technique employed to identify, screen, and filter related publications on MDLES research recovered 969 documents comprising articles, conference papers, and reviews published in English. The results showed that the publications count on the topic increased from 3 to 385 (or a 12,733.3% increase) along with citations between 2012 and 2022. The high publications and citations rate was ascribed to the MDLES research impact, co-authorships/collaborations, as well as the source title/journals’ reputation, multidisciplinary nature, and research funding. The top/most prolific researcher, institution, country, and funding body on MDLES research are; is Yan Xu, Tsinghua University, China, and the National Natural Science Foundation of China, respectively. Keywords occurrence analysis revealed three clusters or hotspots based on machine learning, digital storage, and Energy Storage. Further analysis of the research landscape showed that MDLES research is currently and largely focused on the application of machine/deep learning for predicting, operating, and optimising energy storage as well as the design of energy storage materials for renewable energy technologies such as wind, and PV solar. However, future research will presumably include a focus on advanced energy materials development, operational systems monitoring and control as well as techno-economic analysis to address challenges associated with energy efficiency analysis, costing of renewable energy electricity pricing, trading, and revenue prediction.
