Abstract: An Adaptive Multi-swarm Backtracking Social Spiders Optimization(AMBSSO) is proposed to solve the complex multimodal function optimization problems of Social Spiders Optimization（SSO） algorithm which has low success rate and convergence precision. The adaptive decision radius is introduced in SSO algorithm to improve the sample population diversity. The spider population is dynamically divided into multiple populations.  Individual spider takes different updating ways according to its fitness. The backtracking evolution strategy is put forward to ensure global searching ability and it is carried out according to evolutionary level based on the selection of global extremum of function. The simulation results show that AMBSSO algorithm has faster convergence speed and higher convergence precision, especially for high-dimensional and multimodal function optimization problems, compared with SSO, PSO and other optimization algorithms.

Key words: social spider optimization algorithm, multi-swarm, multimodal function optimization, adaptation, backtracking

摘要： 针对群居蜘蛛优化（SSO）算法求解复杂多峰函数成功率不高和收敛精度低的问题，提出了一种自适应多种群回溯群居蜘蛛优化（AMBSSO）算法。引入自适应决策半径概念，动态地将蜘蛛种群分成多个种群，种群内适应度不同的个体采取不同的更新方式，提高了种群样本多样性；提出回溯迭代进化策略，在筛选全局极值的基础上，根据进化程度执行回溯迭代更新，保证了算法全局寻优能力。高维多峰函数仿真结果表明，同SSO算法、PSO算法等优化算法相比，AMBSSO算法具有较快的收敛速度和较高的收敛精度，尤其适用复杂高维多峰函数优化问题。

关键词: 群居蜘蛛优化算法, 多种群, 多峰函数优化, 自适应, 回溯