In order to solve complex practical problems, the model of deep learning can not be limited to models such as deep neural networks. To deepen the learning model, we must actively explore various depth models. Based on this, we propose a deep evolutionary algorithm, that is group competition cooperation optimization (GCCO) algorithm. Unlike the deep learning, in the GCCO algorithm, depth is mainly reflected in multi-step iterations, feature transformation, and models are complex enough. Firstly, the bio-group model is introduced to simulate the behavior that the animals hunt for the food. Secondly, according to the rules of mutual benefit and survival of the fittest in nature, the competition model and cooperation model are introduced. Furthermore, in the individual mobility strategy, the wanderers adopt stochastic movement strategy based on feature transformation to avoid local optimization. The followers adopt the variable step size region replication method to balance the convergence speed and optimization precision. Finally, the GCCO algorithm and the other three comparison algorithms are used to test the performance of the algorithm on ten optimization functions. At the same time, in the actual problem of setting up the Shanghai gas station the to improve the timely rate, GCCO algorithm achieves better performance than the other three algorithms. Moreover, Compared to the Global Search, the GCCO algorithm takes less time to achieve similar effects to the Global Search.

Social spider optimization (SSO) is a swarm algorithm designed for solving complex optimization problems. It is an effective approach for searching a global optimum by simulating the cooperative behavior of social-spiders. However, SSO takes too much computation time and shows premature convergence on some problems. In order to accelerate the computation speed and further enhance the search ability, a parallel SSO (PSSO) algorithm with emotional learning is proposed in this paper. First, we develop a parallel structure for the female and male individuals to update their positions, and each individual can be computed in parallel during the search process. Second, an emotional learning mechanism is used to increase swarm diversity which is helpful to improve the search performance. Furthermore, the convergence property and computational complexity of PSSO are discussed in detail. To test the effectiveness of the proposed algorithm, it is applied to solve data clustering problem. The experimental results demonstrate that the overall performance of PSSO is superior to six other clustering algorithms on several standard data sets. In the aspect of search performance, the results obtained by PSSO are better than the comparison algorithms in most used data sets. In the aspect of time performance, the computation time of PSSO is greatly reduced in the parallel computing environment. It is comparable with K-means which is the fastest among the comparison algorithms when the number of processors larger than and equals to 16.

将深度与演化算法结合，提出一种深度演化算法，即群竞争合作优化（GCCO）算法。首先引入生物群模型来模拟群体搜索猎物的自然现象，算法通过多步迭代可简单实现优化问题求解。在生物群模型中，跟随者采用变步长的区域复制方式，平衡了收敛速度与优化精度，随机者采用基于特征变换的随机游走模式，避免陷入局部最优。其次引入竞争模型和合作模型增加算法复杂性，通过群体间的竞争和信息共享，提高算法的搜索性能。算法的数学模型是从群论、动力学以及帝国竞争理论推导出来的，在理论上也分析验证了算法的收敛性。最后在十个优化基准函数上与其他三种优化算法对比测试算法的性能。在解决上海市设立燃气站点提高到场及时率的实际问题中，GCCO算法也取得了比其他算法更好的效果。

In this paper, a clustering algorithm based on emotional preference and migratory behavior (EPMC) is proposed for data clustering. The algorithm consists of four models: the migration model, the emotional preference model, the social group model and the inertial learning model. First, the migration model calculates the probability of individuals being learned, so that individuals can learn from the superior. Second, the emotional preference model is introduced to help individuals find the most suitable neighbor for learning. Third, the social group model divides the whole population into different groups and enhances the mutual cooperation between individuals under different conditions. Finally, the inertial learning model balances the exploration and exploitation during the optimization, so that the algorithm can avoid falling into the local optimal solution. In addition, the convergence of EPMC algorithm is verified by theoretical analysis, and the algorithm is compared with four clustering algorithms. Experimental results validate the effectiveness of EPMC algorithm.

Recently, many evolutionary algorithms (EAs) have been used to solve clustering problem. However, compared to K-means which is a simple and fast clustering algorithm, these EA-based clustering algorithms take too much computation time. In addition, the parameters of most EAs are fixed or dynamical adjustment by a simple method on different datasets, and it will cause that the performance of these algorithms is good on some datasets but bad on others. In order to overcome these disadvantages, a novel parallel object-tracking behavior algorithm (POTBA) based on dynamics is proposed in this paper. The proposed algorithm consists of three different models which are parallel object-tracking model, parameters self-learning model and energy model, respectively. First, the parallel object-tracking model is designed to accelerate the computation speed and avoid local minima. Second, the parameters of POTBA are self-adjusted by the parameters self-learning model. Third, the energy model is introduced to depict energy changes of POTBA during the evolutionary process. The correctness and convergence properties of POTBA are analyzed theoretically. Moreover, the effectiveness and parallelism of POTBA are evaluated through several standard datasets, and the experimental results demonstrate that POTBA exhibits superior overall performance than five other state-of-the-art algorithms. In the aspect of search performance, the results of POTBA are better than other comparison algorithms on most used datasets. In the aspect of time performance, the time overhead of POTBA is significantly reduced through parallel computing. When the number of processors increases to 32, the computation time of POTBA is less or close to K-means which is the fastest comparison algorithm.