Parent-Child Nature-Inspired Method for Cost Optimisation in Computer Networking Infrastructure Projects

Richard Nana Nketsiah, Richard Charles Millham, Israel Edem Agbehadji, Emmanuel Freeman, Simon James Fong

Abstract: The need for effective and affordable computer networking infrastructure projects has grown dramatically due to the rapid evolution of technology, requiring the development of novel optimization techniques. These projects need sophisticated optimization techniques that can strike a balance between project efficiency and cost reduction due to their complexity and dynamic limitations. Although they have yielded some results, traditional optimization techniques—both deterministic and heuristic—frequently find difficulties in adjusting to the dynamic character of these projects. In order to overcome this drawback, the Parent-Child Optimization (PCO) algorithm is presented in this paper. It is a new, naturally inspired technique created specifically to reduce labour expenses and maximize resource allocation in networking projects. The experiment’s findings show that PCO performs better than various classic algorithms, including Teacher-Learner Based Optimization (TLBO), Ant Colony Optimization (ACO), Bat Algorithm (BAT), and Particle Swarm Optimization (PSO). PCO’s ability to allocate experienced and unskilled workers efficiently is demonstrated by the fact that it was able to achieve the lowest labor cost of $56,040, the shortest project length of 30 days, and the greatest project profit of $1,300. These results demonstrate the outstanding performance, resilience, and adaptability of PCO in practical settings, making it a potent instrument for cost optimization in computer networking infrastructure projects and other challenging optimization problems.