Nature has always been a muse for those who dream in art or science. As it goes, optimization algorithms inspired by nature have been widely used to solve various scientific and engineering problems because of their intelligence and simplicity. As a novel nature‐inspired algorithm, the crystal energy optimizer (CEO) is proposed in this article. The proposed CEO is motivated by the following general observation on lake freezing in nature: the dynamics of crystals have possession of parallelism, openness, local interactivity, and self‐organization. It stimulates us to extend a crystal dynamic model in physics to a generalized crystal energy optimizer for traveling salesman problems, so as to exploit the advantages of crystal dynamic system and to realize the aforementioned purposes. The proposed CEO has these advantages: (1) it has the ability to perform large‐scale distributed parallel optimization; (2) it can converge and avoid local optimum; and (3) it is flexible and easy to adapt to a wide range of optimization problems.

Due to the dynamic structure in network topology and absence of a centralized administration in management, a specific routing algorithm satisfying the demands of QoS is required indeed in mobile Ad Hoc networks. A novel Social Group Search Optimizer algorithm is pro-posed by improving the GSO algorithm to a dynamic and discrete algorithm through the introducing of social behaviors. SGSO is divided into search and prey parts, where “search” is on duty to find the optimal solution effectively and “prey” is responsible for adjusting the algorithm to the dynamic change of objective functions. Dynamic Coupling Level is used to divide the Ad Hoc network and corresponding approaches and models based on SGSO are applied to routing algorithm, including the decision factor and local routing table. The convergence and correctness of our algorithm are verified mathematically and extensive experiments have been conducted to evaluate the efficiency and effectiveness of the proposed mechanism in mobile Ad Hoc networks. The results show that SGSO improves packet delivery ratio and reduces average end-to-end latency effectively, especially for large-scale and high-dynamicnetworks.

为了缓解Internet网络拥挤状况,提高用户访问网站的响应速度,从技术上解决由于网络带宽小、用户访问量大、网点分布不均等原因所造成的用户访问网站响应速度慢的问题,提出了一种新的缓存资源分配方法——细胞优化算法。该算法是模仿自然细胞系统功能的一种智能优化方法,其通过模拟细胞内部结构和原理,对细胞核、细胞质的浓度、细胞间的亲和度、细胞优化机制、细胞的动态演化过程建立数学模型。给出了算法的并行计算结构和步骤。最后,通过理论证明、仿真实验与同类算法的比较,验证了算法求解CDN缓,存资源分配问题的有效性。

随着当今世界逐渐从信息化转型为数据化，模式识别和数据挖掘等领域面临越来越大的挑战.爆炸式增大的数据量使得特征选择过程成为大数据模式识别等领域必不可少的环节.受动物界资源争夺行为启发，在由特征选择模型转变为资源分配问题模型中加入个体的资源争夺行为，提出多群体公平算法(multi-colony fairness algorithm, MCFA)对该行为进行评判和处理，用以取得更优的分配方案(即更优特征子集)，其有机融合随机搜索和启发式搜索，且将filter方法和wrapper方法相结合，降低计算量的同时获得更高的分类准确率.对提出的多群体公平算法进行了分析，从理论上证明了算法的收敛性和有效性；UCI机器学习数据库数据集与4种经典特征选择算法:顺序前向搜索算法(sequential forward selection, SFS)、顺序后向搜索算法(sequential backward selection, SBS)、顺序前向浮动搜索算法(sequential floating forward selection, SFFS)、顺序后向浮动搜索算法(sequential floating backward selection, SBFS)和3种主流特征选择算法：相关性-冗余度特征选择算法(relevance-redundancy feature selection, RRFS)、最大相关最小冗余算法(minimal-redundancy-maximal-relevance, mRMR)、ReliefF算法的对比实验表明，提出的多群体公平算法能够有效选择规模和性能都比较好的特征子集.

机器人学是现在及未来科技发展的重点，路径规划是机器人学中的一个重要课题.生物界一些群居动物有严格的等级制度和职责分工，受社会群居动物行为启发，提出社会群体搜索算法(social group search algorithm， SGSO).社会群体搜索算法对群体的分类及信息反馈机制——领导-追随机制的制定，降低了早熟的概率，交叉变异和淘汰机制的引入增加了搜索范围，减少了陷入局部最优的可能.同时，对提出的社会群体搜索算法进行了分析，从理论上证明了算法的收敛性；将社会群体搜索算法应用于机器人路径规划进行仿真，从实验中验证了算法的有效性，并与遗传算法和粒子群算法比较，进一步证明了社会群体搜索算法在机器人路径规划问题中的有效性和高效性.