Abstract
QUasi-Affine TRansformation Evolutionary algorithm (QUATRE) is a new optimization algorithm based on population for complex multiple real parameter optimization problems in real world. In this paper, a novel multi-group multi-choice communication strategy algorithm for QUasi-Affine TRansformation Evolutionary (MM-QUATRE) algorithm is proposed to solve the disadvantage that the original QUATRE is always easily to fall into local optimization in the strategy of updating bad nodes with multiple groups and multiple choices. We compared it with other intelligent algorithms, the most advanced PSO variant, parallel PSO (P-PSO) variant, native QUATRE and parallel QUATRE (P-PSO) under CEC2013 large-scale optimization test suite. Thus, the performance of MM-QUATRE was verified. The conclusion that the MM-QUATRE algorithm is superior to other intelligent algorithms is proved by the experimental results. In addition, the application results of MM-QUATRE algorithm (MM-QUATRE-RSSI) based on RSSI in WSN node localization were analyzed and studied. The results appear that this method has higher localization accuracy than other similar algorithms.
Highlights
In the past decades, the problem of global optimization has attracted the attention of many scholars
During the implementation of MM-QUasi-Affine TRansformation Evolutionary algorithm (QUATRE) algorithm, the group was divided into four subgroups to improve the diversity of optimization ability
Each subgroup completes iterative evolution independently, and carries out inter-group communication for 50 times every iteration, and updates the bad point status of each subgroup according to the communication information, so that the bad point can regain the ability to search for optimization
Summary
The problem of global optimization has attracted the attention of many scholars. Du et al.: QUATRE With Communication Schemes for Application of RSSI in Wireless Sensor Networks optimization(PSO) algorithm is generated [14]–[16]. QUATRE [21]–[24] is a novel proposed global optimization algorithm for evolutionary structure. For purpose of enhance the global optimization performance of QUATRE and avoid falling into the local optimal position, we propose a new improved strategy of Multi-group and Multi-choice in this paper. Xr1, Xr2, Xr3, Xr4, Xr5 represent the random matrix generated by random permutation of the matrix X. xgbest,G denote the position vector of the globally optimal particle at the Gth iteration.
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