Abstract—The collective intelligent behaviors of insects or animal groups in nature have maintained the survival of the species for thousands of years. In this paper, a novel swarm intelligence algorithm called the social group entropy optimization (SGEO) algorithm is proposed for solving optimization tasks. The proposed algorithm is based on the social group model, the status optimization model, and the entropy model, which are the main contributions of this paper. First, the social group model and the feedback mechanism between Leaders and Followers are developed to reduce the probability of local optimum. Second, the status optimization model is described to reveal the changing rule about the population behavior states, to support the conversion between different social behaviors during evolution, to promote the algorithm to optimize quickly, and to avoid local optimization. Third, the entropy model is introduced to analyze the entropy of social groups, the change rule of difference entropy, and to set the information entropy as behavior’s criterion of state optimization. In addition, the mathematical model of the SGEO is deduced from the group theory, matter dynamics, and the information entropy theory. The convergence and parallelism of it have been analyzed and verified theoretically. Moreover, to test the effectiveness of the SGEO, it is used to solve benchmark functions’problems that are commonly considered within the literature of evolutionary algorithms. Experimental results are compared with those of three other state-of-the-art algorithms. The superior performance of the SGEO validates its effectiveness and efficiency for the optimization problems, especially for the high-dimension problems.

In general, the existing evolutionary algorithms are prone to premature convergence and slow convergence in coping with combinatorial optimization problems. So an intelligent optimization algorithm called physarum-energy optimization algorithm (PEO) is proposed and put TSP as the carrier in this paper. This algorithm consists of four parts: the physarum biological model, the energy model, the age factor model and the stochastic disturbance model. First, the high parallelism of PEO is enlightened from the physarum’s low complexity and high parallelism. Second, we present an energy mechanism model in PEO, which is mainly to develop the shortcomings of existing algorithm, such as slow convergence and lack of interaction capability. Third, inspired by the characteristic of ants’ spatiotemporal variations, the age factor mechanism is introduced to raise search capacity, which can control the convergence speed and precision ability of PEO. In addition, in order to avoid premature convergence, the stochastic disturbance mechanism is adopted into PEO. And also the feasibility and convergence of PEO has been analyzed and verified theoretically. Moreover, we compare the algorithm and other algorithms to TSPs of diverse scope. The experiment results show that PEO has the advantages of excellent global optimization, high optimization accuracy and high parallelism and is significantly better than other algorithms.

随着人工智能和大数据的迅猛发展，大数据的爆炸式增长和问题的复杂性分布导致对并行智能处理的要求日趋迫切.传统的理论模型和技术方法面临严峻挑战，受自然界启发的物理学法则和生物学方法逐渐成为研究热点.受多头绒泡菌的生长觅食等行为启发，提出了一种基于能量机制的多头绒泡菌动力学算法(physarum-energy dynamic optimization algorithm, PEO).该算法以多头绒泡菌算法为基础，根据其动力学特征，引入能量机制，以改进现有的多头绒泡菌算法全局信息交互能力差等缺点.此外，PEO引入了年龄因子的概念和扰动机制，以控制算法在不同阶段的寻优能力和收敛速度，并从理论角度对算法模型的收敛性进行证明.最后，通过在TSP数据集上实验证明算法在不同规模数据集的有效性和收敛性，并进行了参数分析.与其他的优化算法的对比实验数据表明，PEO在面对复杂问题的求解速度和收敛速度明显优于其他的优化算法，具有高精度和快收敛的特性.

The matter state change, which is a common phenomenon in nature, shows the process that the matter how to reach the optimal state in the environment. This paper presents a novel particle state change model (PSCM) inspired by mimicking the process of the matter state change. Based on PSCM, a novel particle state change (PSC) algorithm is proposed for solving continuous optimization problems. As a new algorithm, PSC has many differences from other similar nature-inspired algorithms in terms of the basic principle models, mathematical formalization and properties. This paper considers three states of the matter, namely gas state, liquid state and solid state. In a certain circumstance, the matter always converts from an unstable state into a stable state. It is similar to find the optimal solution of an optimization problem. The proposed algorithm also has the advantages in the respects of higher intelligence, effectiveness and lower computation complexity. And the convergence property of PSC is discussed in detail. In order to illustrate the ability of solving optimization problems in continuous domain, the new proposed algorithm is tested on basic function optimization, CEC2016 single-objective real-parameter numerical optimization and CEC2016 learning-base real-parameter single-objective optimization, and compared with eleven existing algorithms. The numerous simulations have shown the effectiveness and suitability of the proposed approach.

As the world gradually transforms from an information world to a data-driven world, areas of pattern recognition and data mining are facing more and more challenges. The process of feature subset selection becomes a necessary part of big data pattern recognition due to the data with explosive growth. Inspired by the behavior of grabbing resources in animals, this paper adds personal grabbing-resource behavior into the model of resource allocation transformed from the model of feature selection. Multi-colony fairness algorithm (MCFA) is proposed to deal with grabbing-resource behaviors in order to obtain a better distribution scheme (i.e., to obtain a better feature subset). The algorithm effectively fuses strategies of the random search and the heuristic search. In addition, it combines methods of filter and wrapper so as to reduce the amount of calculation while improving classification accuracies. The convergence and the effectiveness of the proposed algorithm are verified both from mathematical and experimental aspects. MCFA is compared with other four classic feature selection algorithms such as sequential forward selection, sequential backward selection, sequential floating forward selection, and sequential floating backward selection and three mainstream feature selection algorithms such as relevance–redundancy feature selection, minimal redundancy–maximal relevance, and ReliefF. The comparison results show that the proposed algorithm can obtain better feature subsets both in the aspects of feature subset length which is defined as the number of features in a feature subset and the classification accuracy. The two aspects indicate the efficiency and the effectiveness of the proposed algorithm.