The development of the Internet of Things brings new opportunities and challenges for sensor networks. The scale of sensor networks tends to be larger. And the fusion rules need to be intelligent. In this paper, we propose a new Internet of Things group search optimizer (ITGSO) to solve intelligent information fusion problems in the high-dimensional multi-sensor networks. ITGSO is inspired by the latest research achievement about leader decision in Nature and works about social coordination, which mainly consists of three parts: basic group search optimizer, binary group search optimizer, and social decision model. With ITGSO, we need less time to obtain minimum Bayes cost than particle swarm optimization. And information of uncertain social intelligent problems can be fused. In this paper, we give the theoretical basic of ITGSO and proved its validity via mathematical analysis and simulation results.

The development of the Internet of Things brings new opportunities and challenges for sensor networks.The scale of sensor networks tends to be larger. And the fusion rules need to be intelligent. In this paper, we propose a new Internet of Things group search optimizer (ITGSO) to solve intelligent information fusion problems in the high-dimensional multi-sensor networks. ITGSO is inspired by the latest research achievement about leader decision in Nature and works about social coordination, which mainly consists of three parts: basic group search optimizer, binary group search optimizer and social decision model. With ITGSO, we need less time to obtain minimum Bayes cost than particle swarm optimization. And information of uncertain social intelligent problems can be fused. In this paper, we give the theoretical basic of ITGSO and proved its validity via mathematical analysis and simulation results.

Creative thinking, which plays an essential role in the progress of human society, has an outstanding problem-solving ability. This paper presents a novel creativity-oriented optimization model (COOM) and algorithm (COOA) inspired by the creative thinking process. At first, COOM is constructed by simplifying the procedure of creative thinking while retaining its main characteristics. And then, COOA is presented for continuous optimization problems. It is a realization of COOM. As a new nature-inspired algorithm, COOA is different from other similar algorithms in terms of the basic principle, mathematical formalization and properties. Features of the COOM and the corresponding algorithm include a powerful processing ability for the complex problems, namely high-dimensional, highly nonlinear and random problems. The proposed approach also has the advantages in terms of the higher intelligence, effectiveness, parallelism and lower computation complexity. The properties of COOA, including convergence and parallelism, are discussed in detail. The numerous simulations on the CEC-2013 real-parameter optimization benchmark functions’ problems have shown the effectiveness and parallelism of the proposed approach.

Creative thinking, which plays an essential role in the progress of human society, has an outstanding problem-solving ability. This paper presents a novel creativity-oriented optimization model (COOM) and algorithm (COOA) inspired by the creative thinking process. At first, COOM is constructed by simplifying the procedure of creative thinking while retaining its main characteristics. And then, COOA is presented for continuous optimization problems. It is a realization of COOM. As a new nature-inspired algorithm, COOA is different from other similar algorithms in terms of the basic principle, mathematical formalization and properties. Features of the COOM and the corresponding algorithm include a powerful processing ability for the complex problems, namely high-dimensional, highly nonlinear and random problems. The proposed approach also has the advantages in terms of the higher intelligence, effectiveness, parallelism and lower computation complexity. The properties of COOA, including convergence and parallelism, are discussed in detail. The numerous simulations on the CEC-2013 real-parameter optimization benchmark functions’ problems have shown the effectiveness and parallelism of the proposed approach.

旅行商问题（ＴｒａｖｅｌｉｎｇＳａｌｅｓｍａｎＰｒｏｂｌｅｍ，ＴＳＰ）是ＮＰ完全问题中最为著名的问题，它易于陈述而难于求解，至今尚未找到准确有效的求解大规模ＴＳＰ问题的方法．文中提出了能求出ＴＳＰ有效近似最优解的新的蚊子追踪（ＭｏｓｑｕｉｔｏＨｏｓｔＳｅｅｋｉｎｇ，ＭＨＳ）算法，证明了蚊子的目标追踪行为和ＭＨＳ数学模型的一致性、蚊子追踪算法的收敛性，并通过理论证明确定了ＭＨＳ算法中各参数的选择范围．蚊子追踪算法是一个全新的仿生算法．文中以ＴＳＰ问题为载体，详细提出了蚊子追踪算法的动机、生物学模型、数学模型、算法、理论基础（数学证明）及大量实验结果．从理论和实验两方面证明了蚊子追踪算法能够求出ＴＳＰ问题理论上的优化解