An improved discreet Jaya optimisation algorithm with mutation operator and opposition-based learning to solve the 0-1 knapsack problem
Mohammad Subhi Al Batah, Mohammed Riyad Al Eideh
Abstract
The 0-1 knapsack problem, a prevalent combinatorial optimisation problem, aims to maximise the benefits of a chosen subset of items while ensuring the total capacity is not exceeded. This paper proposes an improved discreet Jaya (IDJaya) algorithm to solve 0-1 knapsack problems. IDJaya includes opposition-based learning (OBL), Jaya algorithm, transfer functions, mutation operator, repair method, and fitness function. OBL is used to improve the diversity of the initial population, while the mutation operator is used to enhance the search capabilities to bypass local optima issues. Transfer functions are used to convert the Jaya algorithm from a continuous to a binary version. The repair method is used to handle infeasible solutions, and the fitness function is used to assess each solution. IDJaya's performance had been tested on a range of benchmark knapsack problems, demonstrating that IDJaya could be an effective alternative solution for various 0-1 knapsack problems.