LEVERAGING HYBRID OF METAHEURISTIC AND EVOLUTIONARY ALGORITHMS FOR OPTIMIZATION IN NIGERIA'S SPACE PROGRAM
This work assesses the potential of the synergy between hybrid metaheuristic and evolutionary algorithms for optimizing Nigeria's space program, administered by NASRDA. The problem addressed lies in the absence of published research directly applying such advanced optimization techniques within Nigeria�s space ecosystem, despite their proven global success and the urgent need for improved efficiency in satellite design, mission planning, and resource allocation. Efficient optimization is vital for satellite design, mission planning, and data analysis, as Nigeria seeks indigenous space capabilities for socio-economic growth. The aim of the research is to evaluate the applicability of hybrid metaheuristic and evolutionary algorithms toward enhancing operational performance and innovation in Nigeria�s space program. By balancing solution exploration and refinement, hybrids of metaheuristics and evolutionary algorithms excel at solving such complex problems. The methodology adopted involved a focused literature review, identification of research gaps, and conceptual modeling using a hybrid approach in line with the Nigeria space context. Although, findings show, that these strategies, have been successfully implemented, in related fields, such as aerospace, logistics, and energy systems, their application in Nigeria�s space program, stays largely undocumented, showing a huge opportunity, for performance improvement. The lack of published research, on their specific application, in Nigeria's space context, reveals a significant gap, in this study. The work recommends target assessment, benchmarking initiatives and capacity building, based on these findings, to promote the integration of hybrid optimization methods, into Nigeria�s space operations. This would lead to improved efficiency and innovation, bridging the literature gap and advance space technology objectives.
Authors : Saliu, A.S., Mathew, O.A., Iliya, S.Z., Abdulkareem, H., Shobowale, K.O., Abubakar, U. and Alioke, O.C.
Category : Open Access Volume (Issue) : 11(2) Date Uploaded : 3rd December 2025