AHPSO: Altruistic Heterogeneous Particle Swarm Optimisation Algorithm for Global Optimisation

This paper introduces a new particle swarm optimisation variant: the altruistic heterogeneous particle swarm optimisation algorithm (AHPSO). The algorithm conceptualises particles as energy-driven agents with bio-inspired altruistic behaviour. In our approach, particles possess a current energy level and an activation threshold and are in one of two possible states (active or inactive) depending on their energy levels at time tau. The idea of altruism is used to form lending-borrowing relationships among particles to change an agent's state from inactive to active, and the main search mechanism exploits this idea. Diversity in the swarm, which prevent premature convergence, is maintained via agent states and the level of altruistic behaviour particles exhibit. The performance of AHPSO was compared with 11 metaheuristics and 12 state-of-the-art PSO variants using the CEC'17 and CEC'05 test suites at 30 and 50 dimensions. The AHPSO algorithm outperformed all 23 comparison algorithms on both benchmark test suites at both 30 and 50 dimensions.