Data for 'Two New Bio-inspired Particle Swarm Optimisation Algorithms for Single-Objective Continuous Variable Problems Based on Eavesdropping and Altruistic Animal Behaviours' (2024)

Data For paper submitted to <i>Biomimetics </i>Sept 2024<b>Abstract</b>This paper presents two novel bio-inspired particle swarm optimisation (PSO) variants: biased eavesdropping PSO (BEPSO) and altruistic heterogeneous PSO (AHPSO). These algorithms are inspired by types of group behaviour found in nature that have not previously been exploited in search algorithms. The primary search behaviour of the BEPSO algorithm is inspired by eavesdropping behaviour observed in nature coupled with a cognitive bias mechanism that enables particles to make decisions on cooperation. The second algorithm, AHPSO, conceptualises particles in the swarm as energy-driven agents with bio-inspired altruistic behaviour which allows the formation of lending- borrowing relationships. The mechanisms underlying these algorithms provide new approaches to maintaining swarm diversity which contributes to preventing premature convergence. The new algorithms were tested on the 30, 50 and 100-dimensional CEC’13, CEC’14 and CEC’17 test suites, 1and various constrained real-world optimisation problems, against 13 well-known PSO variants and the CEC competition winner, differential evolution algorithm L-SHADE. The experimental results show that both algorithms, BEPSO and AHPSO, provide very competitive performance on the unconstrained test suites and the constrained real-world problems. They were significantly better than most other PSO variant on most problem sets and no other comparator algorithm was significantly better than either of them on any of the 50 and 100-d problem sets.

本数据集对应2024年9月投稿至《Biomimetics》期刊的论文相关数据。**摘要**：本文提出两种新型仿生粒子群优化（Particle Swarm Optimisation, PSO）变体算法：偏置窃听粒子群优化（Biased Eavesdropping PSO, BEPSO）与利他异构粒子群优化（Altruistic Heterogeneous PSO, AHPSO）。上述算法的设计灵感来源于此前未被搜索算法所利用的自然群体行为模式。BEPSO算法的核心搜索行为借鉴了自然界中观察到的窃听行为，并结合认知偏差机制，使粒子能够基于该机制进行合作决策。第二种算法AHPSO则将群体中的粒子建模为受能量驱动的智能体，其行为灵感来源于自然中的利他行为，可在粒子间构建借贷互助关系。上述算法所采用的机制为维持群体多样性提供了新方法，进而有效避免早熟收敛。本文将所提两种算法与13种经典PSO变体算法以及CEC竞赛冠军算法——差分进化L-SHADE（Differential Evolution L-SHADE）进行对比，在30维、50维与100维的CEC’13、CEC’14及CEC’17测试集，以及各类带约束的实际工程优化问题上开展了对比实验。实验结果表明，BEPSO与AHPSO两种算法在无约束测试集与带约束实际问题中均展现出极具竞争力的性能：在多数测试集上，二者的性能显著优于绝大多数其他PSO变体算法；而在50维与100维的高维测试问题中，无任何对比算法能够显著优于二者之一。