Hybrid Cat-Particle Swarm Optimization Algorithm on Bounded Knapsack Problem with Multiple Constraints

Abstract

Optimization problems have become interesting problems to discuss, including the knapsack problem. There are many types and variations of knapsack problems. In this paper, the authors introduce a new hybrid metaheuristic algorithm to solve the modified bounded knapsack problem with multiple constraints we call it modified bounded knapsack problem with multiple constraints (MBKP-MC). Authors combine two popular metaheuristic algorithms, Particle Swarm Optimization (PSO) and Cat Swarm Optimization (CSO). The algorithm is named Hybrid Cat-Particle Swarm Optimization (HCPSO). The results of the implementation of the algorithm are compared with PSO and CSO algorithms. Based on the experimental results, it is known that the HCPSO algorithm is suitable and can reach to goodquality solution within a reasonable computation time. In addition, the new proposed algorithm performs better than the PSO and CSO on all MBKP-MC data used.

An efficient optimizer for the 0/1 knapsack problem using group counseling

“…Researchers have suggested several evolutionary optimization strategies for single-objective and multi-objective optimization problems ( Steuer, 1986 ). These include the Adaptive neuro-fuzzy inference system-evolutionary algorithms hybrid models (ANFIS-EA) ( Roy et al, 2020 ), multi-objective optimization of grid-connected PV-wind hybrid system ( Barakat, Ibrahim & Elbaset, 2020 ), ant colony optimization (ACO) ( Dorigo, Birattari & Thomas, 2006 ), evolution strategy (ES) ( Mezura-Montes & Coello Coello, 2005 ), particle swarm optimization (PSO) ( Janga Reddy & Nagesh Kumar, 2021 ; Coello Coello, Pulido & Lechuga, 2004 ), genetic algorithm ( Deb et al, 2002 ), genetic programming (GP) ( Mugambi & Hunter, 2003 ), evolutionary programming (EP) ( Fonseca & Fleming, 1995 ), differential evolution (DE) ( Storn & Price, 1995 ), group counseling optimizer ( Eita & Fahmy, 2010 ; Ali & Khan, 2013 ), comprehensive parent selection-based genetic algorithm (CPSGA) ( Ali & Khan, 2012 ), whale optimization algorithm ( Masadeh, 2021 ), binary particle swarm optimization algorithm ( Sun et al, 2021 ), hybrid cat-particle swarm optimization algorithm ( Santoso et al, 2022 ), An enhanced binary slime mold algorithm ( Abdollahzadeh et al, 2021 ), improving flower pollination algorithm ( Basheer & Algamal, 2021 ), and 0/1 knapsack problem using genetic algorithm ( Singh, 2011 ), are some of the most common evolutionary optimization techniques. The group counseling optimizer (GCO) ensures uniqueness to prevent premature convergence.…”