Pigeon-inspired optimization (PIO) is a new type of intelligent algorithm. It is proposed that the algorithm simulates the movement of pigeons going home. In this paper, a new pigeon herding algorithm called compact pigeon-inspired optimization (CPIO) is proposed. The challenging task for multiple algorithms is not only combining operations, but also constraining existing devices. The proposed algorithm aims to solve complex scientific and industrial problems with many data packets, including the use of classical optimization problems and the ability to find optimal solutions in many solution spaces with limited hardware resources. A real-valued prototype vector performs probability and statistical calculations, and then generates optimal candidate solutions for CPIO optimization algorithms. The CPIO algorithm was used to evaluate a variety of continuous multi-model functions and the largest model of hydropower short-term generation. The experimental results show that the proposed algorithm is a more effective way to produce competitive results in the case of limited memory devices.

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

At present, the probability density of most chaotic systems is unknown, and the statistical characteristics of chaotic sequences cannot be described by the probability density of chaotic maps. This paper constructs a class of quadratic polynomial chaotic maps with three system parameters, which are topologically conjugated with Tent maps. The probability density functions of this kind of chaotic maps are given. Then, an arcsine function is designed to transform the chaotic sequence generated by the quadratic polynomial chaotic map into a new random sequence, which obeys the uniform distribution on the interval (−0.5, 0.5). In order to show the application of the new uniform random numbers, the applications of it in generating random arrangement, Gaussian measurement matrix of compressed sensing, and pseudo random number generator are discussed.INDEX TERMS Quadratic polynomial chaotic maps, probability density function, random arrangement, Gaussian measurement matrix, pseudo random. number generator.

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