School of Information and Communication Engineering, Hainan University, No. 58 Renmin Avenue, Haikou, 570028, Hainan, China.
School of Science, Hainan University, No. 58 Renmin Avenue, Haikou, 570028, Hainan, China.
Sci Rep. 2022 Apr 8;12(1):5962. doi: 10.1038/s41598-022-09800-x.
Swarm intelligence algorithm is an important evolutionary computation method that optimizes the objective function by imitating the behaviors of various organisms in nature. A two-stage swarm intelligence algorithm named spider pheromone coordination algorithm (SPC) is proposed in this paper. SPC tries to explore as many feasible solutions as possible on the cobweb at the positioning stage. It simulates the release and reception of different pheromones between spiders at the hunting stage, and then spiders move towards prey under the co-action of winds and pheromones. Different from the existing algorithms, SPC simulates the process that spiders accomplish intra-species communications through different pheromones and considers the impact on spider wind movement. A large number of typical benchmark functions are used in comparative numerical experiments to verify the performances of SPC. Experiments are made to compare SPC with a series of swarm intelligence algorithms, showing that SPC has higher convergence accuracy and stronger global searchability, effectively keeping the diversity of feasible solutions.
群体智能算法是一种重要的进化计算方法，通过模仿自然界中各种生物的行为来优化目标函数。本文提出了一种名为蜘蛛信息素协同算法（SPC）的两阶段群体智能算法。SPC 在定位阶段试图在蛛网中尽可能多地探索可行解。它在狩猎阶段模拟了蜘蛛之间不同信息素的释放和接收，然后蜘蛛在风和信息素的共同作用下向猎物移动。与现有算法不同，SPC 模拟了蜘蛛通过不同信息素完成种内通信的过程，并考虑了对蜘蛛风运动的影响。大量典型的基准函数被用于比较数值实验，以验证 SPC 的性能。实验将 SPC 与一系列群体智能算法进行了比较，结果表明 SPC 具有更高的收敛精度和更强的全局搜索能力，有效地保持了可行解的多样性。
