Wireless sensor network (WSN) can effectively help us monitor the surrounding environment and prevent the occurrence of some natural disasters earlier, but we can only get the information of the surrounding environment correctly if we know the locations of nodes. How to know the exact positions of nodes is a strict challenge in WSN. Intelligent computing algorithms have been developed in recent years. They easily solve complex optimization problems, especially for those that cannot be modeled mathematically. This paper proposes a novel algorithm, named parallel whale optimization algorithm (PWOA). It contains two information exchange strategies between groups, and it significantly enhances global search ability and population diversity of the original whale optimization algorithm (WOA). Also, the algorithm is adopted to optimize the localization of WSN. Twenty-three mathematical optimization functions are accustomed to verifying the efficiency and effectiveness of the novel approach. Compared with some existing intelligent computing algorithms, the proposed PWOA may reach better results.

Two new hybrid algorithms are proposed to improve the performances of the meta-heuristic optimization algorithms, namely the Grey Wolf Optimizer (GWO) and Shuffled Frog Leaping Algorithm (SFLA). Firstly, it advances the hierarchy and position updating of the mathematical model of GWO, and then the SGWO algorithm is proposed based on the advantages of SFLA and GWO. It not only improves the ability of local search, but also speeds up the global convergence. Secondly, the SGWOD algorithm based on SGWO is proposed by using the benefit of differential evolution strategy. Through the experiments of the 29 benchmark functions, which are composed of the functions of unimodal, multimodal, fixed-dimension and composite multimodal, the performances of the new algorithms are better than that of GWO, SFLA and GWO-DE, and they greatly balances the exploration and exploitation. The proposed SGWO and SGWOD algorithms are also applied to the prediction model based on the neural network. Experimental results show the usefulness for forecasting the power daily load. Appl. Sci. 2019, 9, 4514 2 of 22 the process. The algorithm is an effective way to solve global optimization problems, and it has the characteristics of generality, stability, and fast convergence. It includes two criteria, exploration and exploitation. Exploitation reflects the ability of finding the best around a good range, while exploration reflects the ability of searching for new range. At the beginning, it should search the whole range as much as possible, then through using exploitation it searches more carefully around the good solution. But they are contradictory. Too small exploration leads to convergence too fast and easy falling into local optimum; however, too small exploitation makes the algorithm converge too slowly.The No Free Lunch (NFL) theorem considers that there is no meta-heuristic algorithm applying for all optimization problems [1,2]. In other words, an algorithm shows very promising results on a set of issues, but it doesn't perform well on another set of issues. So it needs putting forward a new algorithm to get high performance in certain specific areas.Over the past decades, a large number of meta-heuristics are inspired by natural behaviors [3-5], such as, Genetic Algorithm (GA) [6][7][8], Differential Evolution (DE) Algorithm [9-12], Grey Wolf Optimizer (GWO) Algorithm [13-17], Particle Swarm Optimization (PSO) Algorithm [18-21], Artificial Bee Colony (ABC) Algorithm [22-24], Cat Swarm Optimization (CSO) [25-27], Artificial Fish Swarm Algorithm (AFSA) [28,29], Ant Colony Optimization (ACO) Algorithm [30-34], Shuffled Frog Leaping Algorithm (SFLA) [35-40], Biogeography Based Optimization (BBO) Algorithm [41-43], QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm [44][45][46][47] and so on. Because they all have some defects, many researchers also introduce hybrid algorithms to improve the defects [48][49][50][51][52][53][54][55].The rest of the paper is organized as follows: some related research works are described in the Secti...

In this paper, a new intelligent computing algorithm named Enhanced Black Hole (EBH) is proposed to which the mutation operation and weight factor are applied. In EBH, several elites are taken as role models instead of only one in the original Black Hole (BH) algorithm. The performance of the EBH algorithm is verified by the CEC 2013 test suit, and shows better results than the original BH. In addition, the EBH and other celebrated algorithms can be used to solve node coverage problems of Wireless Sensor Network (WSN) in 3-D terrain with satisfactory performance.

The mobile sensor network can sense and collect the data information of the monitored object in real time in the monitoring area. However, the collected information is meaningful only if the location of the node is known. This paper mainly optimizes the Monte Carlo Localization (MCL) in mobile sensor positioning technology. In recent years, the rapid development of heuristic algorithms has provided solutions to many complex problems. This paper combines the compact strategy into the adaptive particle swarm algorithm and proposes a compact adaptive particle swarm algorithm (cAPSO). The compact strategy replaces the specific position of each particle by the distribution probability of the particle swarm, which greatly reduces the memory usage. The performance of cAPSO is tested on 28 test functions of CEC2013, and compared with some existing heuristic algorithms, it proves that cAPSO has a better performance. At the same time, cAPSO is applied to MCL technology to improve the accuracy of node localization, and compared with other heuristic algorithms in the accuracy of MCL, the results show that cAPSO has a better performance.

This paper proposes a novel hybrid algorithm named Adaptive Cat Swarm Optimization (ACSO). It combines the benefits of two swarm intelligence algorithms, CSO and APSO, and presents better search results. Firstly, some strategies are implemented to improve the performance of the proposed hybrid algorithm. The tracing radius of the cat group is limited, and the random number parameter r is adaptive adjusted. In addition, a scaling factor update method, called a memory factor y, is introduced into the proposed algorithm. They can be learnt very well so as to jump out of local optimums and speed up the global convergence. Secondly, by comparing the proposed algorithm with PSO, APSO, and CSO, 23 benchmark functions are verified by simulation experiments, which consists of unimodal, multimodal, and fixed-dimension multimodal. The results show the effectiveness and efficiency of the innovative hybrid algorithm. Lastly, the proposed ACSO is utilized to solve the Vehicle Routing Problem (VRP). Experimental findings also reveal the practicability of the ACSO through a comparison with certain existing methods.