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A layer-by-layer evolution strategy was proposed to deal with the premature convergence of swarm intelligence as a collaborator with other existing researches based on pre-experiments and simple proofs. For promoting the precision of solution and eviting the premature convergence, the self-adaption system was constructed on the basis of the primal algorithm, operations such as compression, selection and re-initialization using the technology of layer-by-layer, and the social information was used including the compressed research space and the optimal solution. The improvements of precision of solution and the vitality of terminal individuals can be found in results of simulation experiments with benchmark functions.

The Fruit fly Optimization Algorithm (FOA) is easy to fall into premature convergence due to the decrease of population diversity in the process of optimization. In order to overcome the problem, a multiple subgroups fruit fly optimization algorithm based on sequential quadratic programming local search (MFOA-SQP) is proposed. The fruit flies are assigned to multiple subgroups and the mutual learning between subgroups adjusts the step by introducing the inertia weight and learning factors in particle swarm optimization algorithm. The subgroups are reclassified every a certain number of iterations, which can improve population diversity and avoid premature convergence. The best individual is searched by SQP to improve the local fruit fly depth search capability, improving the stability and accelerating the evolution of population in the late iterations. Experiments on 6 benchmark test functions are carried out and an application case that classifies banking customers by the optimized generalized regression neural network is adopted. The results show that the proposed algorithm has superior performance in terms of optimization accuracy and speed, and can effectively improve the classification accuracy of generalized regression neural networks. [ABSTRACT FROM AUTHOR]

For the elite reserved strategy of NSGA- II makes a large number of redundant individuals of high rank as elites to the next generation likely to cause the phenomenon of premature convergence, this paper put forward the improved NSGA-II algorithm! I-NSGA- II ) , and applied it to multi-objective optimization of the traffic signal timing. I-NSGA- II used the method of marking redundant individuals. The elite reserved strategy judged the redundancy individuals through the flag and incorporated it into temporary hierarchy. When the size of new group was insufficient, the method took out corresponding scale redundant individuals and incorporated them into new group after mutation. The experiments show that the I-NSGA-II reduces vehicle and pedestrian delays while ensuring that parking rales and queuing lengths are essentially constant. It proves that the proposed algorithm can improve the comprehensive transportation efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Acta Automatica Sinica is the property of Chinese Academy of Sciences, Institute of Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to overcome the premature convergence of PSO algorithm，this paper developed a new PSO algorithm based on subswarm mutation. This paper proposed the subswarm with random directional vibrating exploit to mutate the global optimal site of the swarm, and it changed the way of random mutation. The mutation based on subswarm enabled the algorithm had excellent local exploit ability and circumvented the premature convergence. It used another mutation on bad particles to enhance the algorithm's global exploit ability，and to expand the searching space. It used high dimension benchmark functions to test the performance of it. The results of comparisons show that the proposed algorithm effectively overcomes the premature convergence and shows better global convergence than other improved algorithms and the optimization of multimodal functions is excellent. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For slow convergence and easiness to trap in local optimum of backtracking search optimization algorithm, this paper presented an improved algorithm. It used Maxwell-Boltzmann distribution to generate mutation scale factor and redesigned the mutation strategy based on Maxwell-Boltzmann distribution, which improved convergence speed effectively. Moreover, it added a selection mechanism to mutation strategy to enhance global search ability, which could avoid premature convergence. Through comparing with mutation strategy of differential evolution and numerical experiments on a suite of standard test functions, the fact shows that the improved algorithm not only has faster convergence speed, but also good global search capability. [ABSTRACT FROM AUTHOR]

Using the Particle Swarm Optimization (PSO) algorithm to solve the problem of virtual network embedding can reduce the consumption of network resource, but it also brings premature convergence. An improved PSO algorithm is proposed, which adds random factors, operates along the original direction, and changes the introduction of history factors to the search process. The proposal not only keeps the instruction of history factors to the search process but also increases the search range, thus relieving, the premature convergence problems to a certain extent. The experimental results demonstrate that the improved PSO algorithm can be applied to virtual network mapping and effectively reduce resource consumption in comparison with the original PSO algorithm. [ABSTRACT FROM AUTHOR]

This paper proposed an improved genetic algorithm for the emergency material dispatching problem (EMDP). It improved canonical genetic algorithm (GA) in two ways: improving crossover operator; improving crossover operator and introducing new chromosomes every iteration simultaneously. It built a mathematical model with the characteristic of multiple vehicle types, multiple kinds of materials, multiple depots and emergency locations, and the objective of the model was minimizing the scheduling completion time. Then it solved the model with canonical GA and improved GA respectively and compare the solutions. The comparison result shows that the improved GA improves premature convergence of canonical GA remarkably, and obtains a more accurate solution. [ABSTRACT FROM AUTHOR]

Aiming at the premature convergence problem of particle swarm optimization algorithm, this paper proposes an improved particle swarm optimization algorithm with adaptive mutation. Based on the variance of the population' s fitness and the current optimal solution size, the algorithm determines the best current variation factor of particles. Motion direction of the particle is changed by using variation factor. Then, the particles move into the adjacent regions to search and determine the new individual extreme and global extreme value. Thus, local optimal solution is avoided. The simulation results show that the adaptive mutation particle swarm optimization algorithm enhances the ability to jump out of local optimal solution, and its global search ability has been improved. Its convergence speed is faster, and can effectively avoid the premature convergence problem. Although the running time is increased, it is still in the acceptable range. A little time of obtaining the global optimal solution spends is worth. [ABSTRACT FROM AUTHOR]

In order to overcome the problems of low convergence precision and easily relapsing into local extremum in basic fruit fly optimization algorithm (FOA), this paper presented an adaptive mutation fruit fly optimization algorithm (FOAAM). During the evolution, in the condition of basic FOA's trapping in local extremum judging from the population's fitness variance and the current optimal, first, it generated M current optimal replicates. Then, it disturbed replicates by a certain probability P Gauss mutation operator. Finally, it optimized mutated replicates again to jump out of local extremum and continue to optimize. Experimental results show that the new algorithm has the advantages of better global searching ability, speeder convergence and more precise convergence. [ABSTRACT FROM AUTHOR]