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Aiming at the shortcomings of whale optimization algorithm (WOA) when solving problems such as high dimension, multi-peak and non-origin of optimal value, such as low convergence accuracy and easy to be captured by local optimum, this paper proposed a whale optimization algorithm based on multi-dimensional variation learning and distributed optimization (MLDOWOA) . Firstly, this algorithm introduced adaptive weights and dominant individual interference to dynamically adjust the direction of individual spiral surround, which improved the global search ability and convergence accuracy of the algorithm. Then, in order to further expand the search range of the algorithm, the improved algorithm used a multi-dimensional variation learning mechanism to adaptively plan the direction of population variation. Finally, it put forward the distributed optimization mechanism to coordinate the step size of search, so as to help the population break through the limitation of search stagnation in the middle and late stage. The test results on 8 high dimensional benchmark functions and 4 fixed dimensional benchmark functions show that, compared with the basic WOA, SSA and the improved WOA algorithms ACWOA, AWOA, MSIWOA, ADWOA, the proposed algorithm has significant advantages in convergence accuracy and ability to cope with high dimensional functions. This paper used the algorithm to tune the parameters of the FOPID controller, and compared the control results with the research results of this engineering problem in recent years, which prove the excellent performance of the proposed algorithm in FOPID parameter tuning problems. [ABSTRACT FROM AUTHOR]

Whale optimization algorithm is a new swarm intelligence optimization algorithm inspired by humpback whale hunting behavior, and its performance is far better than the traditional algorithms. In order to track the latest research results and promote the research in the field of swarm intelligence optimization algorithm, this paper firstly introduced the basic principle of whale optimization algorithm, briefly described its implementation steps, and compared and analyzed its pe1formance characteristics. In view of its shortcomings of slow convergence speed and low convergence accuracy, this paper summarized and analyzed the improved methods including other intelligent optimization algorithms, Levy flight strategy, chaos strategy, as well as their advantages and limitations. Then, it described its applications and effect in combinatorial optimization, image segmentation, data prediction, and so on. Finally, this paper summarized and prospected the future development trend. [ABSTRACT FROM AUTHOR]

Aiming at the defects that the whale optimization algorithm (WOA) is easy to fall into the local optimal solution and the convergence speed is slow, this paper proposed a whale algorithm based on the small hole imaging reverse learning strategy to solve the defects. Firstly, this paper introduced the chaotic sequence generated by Gauss map to replace the original algorithm for increasing the diversity of the population. Secondly, it introduced a small hole imaging reverse learning strategy, on this basis, by combining with the optimal worst reverse learning method, which increased the diversity of the optimal position and improved the ability to jump out of local optimum. Finally, it added a nonlinear convergence factor and a logarithmic probability threshold in the algorithm to balance the exploration and exploitation of the algorithm while preserving the advantages of the whale algorithm. Through the simulation test of 10 benchmark functions, the experimental results show that the improved algorithm has obvious improvement in convergence speed and convergence precision performance. [ABSTRACT FROM AUTHOR]

The standard whale optimization algorithm( WOA ) and some derivative algorithms are studied and experimented to solve the problem of poor results of some examples. It is proved that there is a zero searching preference trap in the " encircling" process of WOA. In addition, the unbalanced search characteristics of chaos optimization algorithm( COA) make it difficult to reconcile the chaos initial population and swarm intelligence optimization process. In order to improve the above defects, this paper selected two chaotic systems and bubble net hunting strategy, and designed a set of fusion optimization algorithm. The algorithm adopted the baseline adaptive oscillation group partition strategy based on fitness to guide the group behavior pattern, gave full play to the role of chaotic system, and balanced the exploration and convergence performance. It used the new algorithm to solve the general/improved examples and engineering application cases. It 's obviously that the performance of the algorithm is better than the contrast group algorithm,and there is no searching preference. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the WOA was easy to fall into local optimum and low convergence precision, this paper proposed an improved whale optimization algorithm (IWOA). The algorithm initialized the population by quasi-reverse learning methods and improved the diversity of the population. Then the algorithm modified the linear convergence factor to a nonlinear convergence factor, which was beneficial to balance the global search ability and local development ability. In addition, the algorithm improved the local search ability of the whale optimization algorithm by increasing the adaptive weight and imp roved convergence precision. Finally, the algorithm adjusted the whale optimization algorithm in time by a random differential mutation strategy to avoid falling into the local optimum. It selected nine benchmark functions in the experiment, and iterated all the algorithms 30 times. The improved whale optimization algorithm compared to the original whale optimization algorithm and five improved whale optimization algorithms, the results show that the mean and standard deviation of the algorithm are better than other algorithms, the convergence curve of the algorithm is also superior to most other algorithms. It shows that the imp roved whale optimization algorithm has the best convergence accuracy and algorithm stability, and the convergence speed is significantly faster than most other improved whale optimization algorithms. [ABSTRACT FROM AUTHOR]

In order to solve the problems of slow search speed, premature convergence and low search accuracy of standard whale optimization algorithm, this paper proposed a hybrid strategy to improve whale optimization algorithm. Firstly, it increased the population diversity by generating the initial population with chaotic map, which laid a foundation for the algorithm global search. Then, by the non-linear strategy, it improved the convergence factor and inertia weight to balance the global exploration, local development ability of the algorithm and accelerated the convergence speed. Finally, according to the variance of the group fitness, it set the threshold performing the mutation operation to avoid the premature convergence of the algorithm. By testing 12 typical benchmark functions in three aspects, the experimental results show that the improved algorithm has a remarkable enhancement in search speed and convergence accuracy. Besides, it has a strong ability to get rid of falling into local optimum. [ABSTRACT FROM AUTHOR]

In order to monitor the real time situation of forest fire, this paper studies the image segmentation algorithm applied to the recognition of forest fire image. The forest fire image with complicated background and interference targets, using the single threshold Otsu method, the segmentation accuracy of fire image is poor. Therefore, the whale optimization algorithm to optimal threshold of multi-threshold image segmentation is studied in this paper. Whale Optimization Algorithm (WOA) is searching for the optimal solution of the high efficiency of the algorithm, which has the features of fast convergence and high accuracy. Aiming at the problem of accurately identifying and segmenting the flame area in forest fire image, WOA is used to optimize fitness degree function of Otsu, and simulation experiments are carried out in the fire image (Red Green Blue, RGB) model. The experimental results show that the proposed algorithm outperforms the traditional single threshold Otsu algorithm, can obtain more accurate segmentation threshold and higher segmentation efficiency, even has strong engineering practicability. [ABSTRACT FROM AUTHOR]

Aiming at the shortages of the basic whale optimization algorithm with low optimization precision, slow convergence speed and easy to fall into local optimum, this paper proposed a new algorithm based on dynamic search and cooperative learning. Firstly, it used equivalent replacement and Faure sequences to enhance the quality of initial solution. Secondly, it improved the population diversity and enhanced the ability to jump out of local optimal solutions through the division of population. Finally, in order to improve the convergence rate and precision, it dynamically adjusted the search strategy of the algorithms according to the population evolutionary information. The experimental results show that the proposed algorithm is much better than basic whale optimization algorithm and its several improved algorithms in optimization performance. [ABSTRACT FROM AUTHOR]

In order to improve the performance of whale optimization algorithm for solving complex function optimization problems, this paper proposed an improved whale optimization algorithm based on adaptive parameters and niche technology. Firstly, the algorithm introduced an adaptive probability threshold to coordinate the global exploration and local development abilities. Then, the algorithm used adaptive position weights to adjust the whale position update formula to improve the convergence speed and search precision. Finally, the algorithm used preselection niche technology to avoid premature convergence. The results on 12 typical benchmark functions show that the improved algorithm has faster convergence speed and higher search precision than other comparison algorithms. It proves that the improvement strategy can effectively improve the performance of the whale optimization algorithm for solving complex function optimization problems. [ABSTRACT FROM AUTHOR]

In order to solve the disadvantage of the traditional whale optimization algorithm, which is slow convergence and easy to fall into local optimum, this paper proposed a mixed strategy based whale optimization algorithm. Firstly, it introduced the nonlinear adjustment strategy to modify the convergence factor, balanced the exploration and exploitation capability and accelerated the convergence speed. Then, it introduced an adaptive weighted coefficient into the position update formula of whales to improve the search precision of the algorithm. Finally, it combined the limit threshold idea of artificial bee colony algorithm to effectively jump out of the local optimum and prevent premature convergence. The results show that the proposed algorithm has better search precision and convergence speed through experiments on different dimensions of 14 benchmark functions. [ABSTRACT FROM AUTHOR]