Human Breakfast Selection Algorithm (HBSA): A Human-Inspired Metaheuristic for Constrained Optimization

In this paper, we propose a new metaheuristic algorithm inspired by human daily breakfast choice behavior, namely the human breakfast choice algorithm (HBSA). When deciding what to eat for breakfast, people often consider multiple goals, constraints, and personal preferences. The algorithm simulates the memory mechanism, preference guidance, contextual adaptation, and hybrid decision-making strategies of human breakfast choices to achieve more effective exploration capabilities in solving combinatorial optimization problems. We apply the algorithm to a typical 0-1 knapsack problem and conduct comparative experiments with genetic algorithms (GA) and particle swarm optimization algorithms (PSO). The results show that the improved HBSA performs better in terms of solution quality and stability.