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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]

In order to solve the problem that the original sparrow search algorithm (Sparrow Search Algorithm, SSA) has reduced population diversity and the weak local development ability makes it difficult to jump out of the local optimum under the condition of approaching the global optimum. This paper proposes a sparrow search algorithm based on tangential flight (tangent flight Sparrow Search Algorithm, tanSSA). First of all, using adaptive t-distribution strategy to improve the finder location update formula can improve the individual sparrow’s optimization ability and prevent the algorithm from maturing. Then, the tangent flight strategy in the tangent search algorithm can enhance the search space ability of the algorithm, and can make the algorithm jump out of the advantage of the local optimal solution. In original sparrow search algorithm, using the tangent flight disturbance strategy to disturb the optimal solution. The combination of these two strategies can effectively improve the exploration and development performance of tanSSA algorithm. Finally, using 12 standard benchmark functions to test and verify the optimization performance of tanSSA algorithm combined with Wilcoxon rank sum test, and compared with the original SSA algorithm, whale optimization algorithm, particle swarm optimization algorithm and adaptive t-distribution SSA algorithm. The experimental results show that the optimization ability and convergence speed of the sparrow search algorithm based on tangent flight have a significant improvement. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of aquila optimization algorithm（AO）, this paper proposed an aquila optimization algorithm using hybrid search strategy. Firstly, the algorithm introduced dynamic adjustment function to balance global exploration and local exploitation; Secondly, it introduced chaotic adaptive weights to enhance the global search capability of the algorithm and accelerate the convergence speed of the algorithm; Thirdly, it introduced a new individual mutation probability coefficient and an improved differential mutation strategy, and used individuals with better fitness values to lead other individuals in the population to carry out search activities, which maintained the diversity of the population and enhanced the ability of the algorithm to jump out of the local optimum. Through the numerical experiment simulation of 8 benchmark test functions, 10 CEC2019 test functions and 1 engineering application problem, The experimental results show that the algorithm has a significant improvement in global convergence speed and optimization accuracy, and has a better ability to jump out of the local optimum. [ABSTRACT FROM AUTHOR]

Aiming at the defects that the GOA is easy to fall into the local optimal solution and the convergence speed is slow, this paper proposed a Fuch chaotic grasshopper algorithm based on pigeon swarm algorithm( PFGOA). Firstly, this paper introduced the Fuch chaotic map to generate initialized population. And then it embedded the sine-cosine operator into the updating formula of the position. At the same time, it introduced pigeon search operator to the optimization process, and through changing the inertia weight c to improve and balance the ability of exploration and development of the algorithm. Finally, it introduced the nonlinear inertia weight to the optimal individual to increase the global search ability of the algorithm, and jumped out of local optimization. Through the simulation test of 11 test functions and Wilcoxon rank sum test, the experimental simulation results show that the PFGOA has better performance, high er precision and better significance . [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of crow search algorithm ( CSA), this paper proposed a crow search algorithm using multi-mode flight ( MFCSA) . Based on the strength of foraging ability, the algorithm divided the population into two groups: strong and weak foraging ability. Those with strong foraging ability adopted the strategy of trailing and tracking the optimal target of the current group, and flied to the vicinity of the current optimal position of the group under the guidance of the group information to carry out search activities, which enhanced the local exploitation ability of the algorithm. Those with weaker foraging ability adopted the two strategies of observing and learning the foraging methods of the strong, and flying away quickly when encountering danger, the former could improve the global exploration ability of the algorithm, and the latter could maintain the diversity of the population. Through the numerical experiment simulation of 15 benchmark test functions and 2 engineering application problems, the results show that the MFCSA has better performance in optimization accuracy, convergence speed, etc., enhances the ability to avoid falling into the local optimum, and has better stability. [ABSTRACT FROM AUTHOR]

This paper proposed curve adaptive and simulation annealing grasshopper optimization algorithm to avoid the disadvantages that the grasshopper optimization algorithm was easy to fall into local optimum, slow convergence speed and poor search accuracy. First of all, this paper introduced the adaptive curve to replace the linear adaptation of the grasshopper optimization algorithm key parameter, which improved the global search ability of the algorithm. Then, on this basis, this paper introduced the simulated annealing algorithm, which had a certain probability of receiving the inferior solution of the grasshopper algorithm to jump out of local optimization and achieve global optimization. Adaptive reduction the range of random solutions of grasshopper position in simulated annealing was beneficial to further improve the ability of grasshopper algorithm's exploitation. Through test function, the experimental results show that the improved new algorithm has higher quality and better convergence speed. [ABSTRACT FROM AUTHOR]

In order to solve the problem that symbiotic organisms search algorithm converge slowly and easy to fall into the local optimum, combining quantum genetic algorithm theory, this paper proposed a real-coded quantum symbiotic organisms search algorithm( RQSOS). First, this paper presented the concept of the difference degree based on the principle of triangular fuzzy number, and constructed a variable component vector and a pair of probability amplitude of a allele in a chromosome that could carry more information and enhance the diversity of the solutions. Then it proposed the mode of rotary learning based on the Archimedes spiral, which strengthened the exploration ability of the solution space. Finally it updated the difference degree based on SOS, and carried out the population learning and mutation operations based on the value of the difference degree which could make the whole population evolution rapidly towards the optimal direction and reduced the probability of falling into local optimum. It was verified by numerical optimization and cloud task scheduling problem, and the simulation results show that the RQSOS algorithm can significantly improve the convergence speed and optimization ability, which is a feasible and effective algorithm. [ABSTRACT FROM AUTHOR]

For the capacitated vehicle routing problem(CVRP),this paper proposed an imperialist competitive algorithm which integrated a split mechanism to solve the problem.Firstly,combined with the characteristics of CVRP,this algorithm used the encoding and decoding strategies based on greedy criteria to switch from the algorithm space to the solution space.Secondly,this algorithm presented an imperialist splitting mechanism to improve the global search ability of the algorithm,and simultaneously combined with the 2-Opt algorithm to enhance the local search ability.Finally,the results of simulation experiments with 25 benchmark examples indicate that:the proposed algorithm can effectively solve CVRP,and the optimization errors of all examples are less than 1.0%.Furthermore,the proposed algorithm is more efficiently than the existing imperialist competitive algorithms,particle swarm optimization algorithm,genetic algorithm and cuckoo search algorithm. [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]