Solving the 0–1 Knapsack Problem Using LAB Algorithm

This paper presents the ability of a novel socio-inspired algorithm, the Leader-Advocate-Believer (LAB) algorithm, to address combinatorial optimization challenges, with a specific focus on solving the 0–1 Knapsack Problem (KP). The Knapsack Problem is classified as a Hard Nondeterministic Polynomial (NP) time problem. In context to the real world, solving the knapsack problem is advantageous in various fields such as resource allocation, logistics, and finance. For example, supply chain management helps optimize cargo loading for maximum profit considering weight constraints, leading to more efficient transportation. The LAB algorithm is situated within the context of social-inspired algorithms, drawing inspiration from cultural interactions, competitive behaviors, and societal dynamics. Several cases of the 0–1 KP which included the Single Knapsack Problems and Multidimensional Knapsack Problems are solved. The paper gives a comprehensive report on the abilities of the algorithm and how it has been designed.