New inspirations in swarm intelligence: a survey
International Journal of Bio-Inspired Computation
Seeker optimisation algorithm for the solution of economic load dispatch problems
International Journal of Bio-Inspired Computation
International Journal of Wireless and Mobile Computing
Hybrid group search optimiser with quadratic interpolation method and its application
International Journal of Wireless and Mobile Computing
IEEE Computational Intelligence Magazine
Training artificial neural networks using APPM
International Journal of Wireless and Mobile Computing
Social emotional optimisation algorithm with emotional model
International Journal of Computational Science and Engineering
Pair-copula estimation of distribution algorithms
International Journal of Computing Science and Mathematics
International Journal of Computing Science and Mathematics
International Journal of Computing Science and Mathematics
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Social emotional optimisation algorithm SEOA is a recently proposed swarm intelligent algorithm by simulating the decision process among human society. In SEOA, each individual denotes one virtual person, and three different kinds of emotions are designed: low-spirited, middle-spirited and high-spirited, then, each person selects the behaviour emotion according to emotional index. In the standard version of SEOA, there are three parameters used to control the influences of personal experiences, social experiences and failure experiences, however, all of them are designed as fixed values. This phenomenon is confused with the nature. In fact, the influences of these experiences are different for different period. For example, individual experiences are more important for the early period, the same as failure experiences, while the social experiences are more important in the later period. Therefore, to meet this phenomenon, a dynamic time-varying strategy is designed. To testify the performance of modified SEOA, three famous benchmarks are chosen, they are Rosenbrock model, Rastrigin model and Griewank model. The dimension is from 30 up to 300. Simulation results show this modification improves the performance significantly especially for multimodal, high-dimensional problems.