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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 problems of uneven resource utilization and long task completion time in complex task group scheduling, this paper constructed a complex task group resource scheduling model to minimize the mean square error of resource load and the task group completion time, and proposed a multi-objective optimization algorithm based on boundary range local search and NSGA-Ⅱ, called BRLSN. The algorithm used an effective coding method and cross mutation operator for iterative optimization, and constructed an elite retention strategy based on local search in boundary region to expand the search scope of the algorithm and preserved good individuals in the population. Experimental results show that compared with other multi-objective algorithms, the convergence and diversity of BRLSN are significantly improved. At the same time, the algorithm convergence speed is faster, the population quality is higher, and the result value of the final objective function is obviously optimized. [ABSTRACT FROM AUTHOR]

In order to fix the integer ambiguity quickly and accurately, this paper proposed a new ambiguity search algorithm based on measurement accuracy constraints to solve the problems of the LAMBDA algorithm, such as the wide search range of the integer ambiguity and the low search efficiency. When the least squares problem got optimally weighted, the algorithm took the carrier phase measurement accuracy as the constraint condition to test the fixed ambiguity solution on the basis of the stan-dard differential evolution （DE） algorithm. This algorithm solved the influence of different satellite altitude angles, and could achieve 99% success rate when solving 3D integer ambiguity. Compared with the MLAMBDA algorithm, the standard DE algorithm and the adaptive weighted differential evolution （AWDE） algorithm, the proposed algorithm further improves the efficiency and success rate of ambiguity resolution. [ABSTRACT FROM AUTHOR]

Targeting to the deficiency of sparrow search algorithm solved the multi-objective problems, and the problems can easily enter the partial optimization and inferior convergence in the counting process, this paper came up with a kind of improved multi-objective sparrow search algorithm. First of all, this paper used a new-type non-dominated sorting to find the Pareto front. Next, it integrated the polynomial mutation and cosine algorithm into species evolution strategy to strengthen its searching ability. It used the species selection method of competition mechanism to decrease the differentiation caused by partial optimized particle and overall optimized particle in the searching process. Finally, this paper compared the improved algorithm and various kinds of multi-objective algorithm in standard test function. The simulation results show that, the convergence and searching ability of the improved algorithm are all superior than other algorithm. Therefore, this algorithm can effectively address the multiple target optimization problem. [ABSTRACT FROM AUTHOR]

For the vehicle routing problem with time window, stochastic travel and service time, and customer classification, this paper constructed a chance constrained programming model, which considered two class customers (ordinary customers and priority customers) and adopted the chance constraint to guarantee the punctual service possibility of priority customers. Meanwhile, this paper designed a variable neighborhood iterated local search algorithm to solve the model, and proposed a minimum waiting time heuristic rule to generate initial solution. Based on Solomon benchmarks, it conducted series of simulation experiments. The results show that the initial solution generation method is valid, the given algorithm can get approximate optimal solutions for both of the deterministic and stochastic problems in a short time, and the customer ratio is positively relative to the number of vehicle usage . The results have a certain reference significance to solve the problem of low customer service level caused by the limited resources and uncertain factors. [ABSTRACT FROM AUTHOR]

In order to improve the safety and smoothness of flight, and solve the problems of excessive turning angle and too many search path nodes in the traditional A• algorithm, this paper proposed a synchronous bi-directional A• search algorithm based on sector field expansion. Firstly, this paper extended the hazardous area boundaries based on the raster map method. Secondly, it designed the A• algorithm based on synchronous bi-directional search, which dynamically defined the target nodes for forward and reverse search. Aiming at the problem of limiting search angles, this paper proposed a sector expansion strategy in 5 x 5 domain, and designed an evaluation function with double weight parameters to reduce the generation of redundant points. Finally, in order to verify the effectiveness of the improved algorithm, this paper selected square and irregular shape hazard areas for simulation. The results show that the improved algorithm searches for a smoother path. Compared with the results of the traditional bi-directional A• algorithm, the length of its search path reduce by 1. 65% and 13. 16%, and the number of its search path nodes reduce by 42. 6% and 46. 81 %, respectively, under different shapes of hazard areas, which has a strong search efficiency. [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]

In the industrial Internet environment, concerning about poor cooperation efficiency and high production cost of intercell scheduling, this paper solved the problem in the case of overlapping placement of machines. Firstly, the paper used the hierarchical network design idea to construct a two-layer directed processing network with machines and manufacturing cells as nodes. And it proposed a multi-objective scheduling model aiming at minimizing the average degree, completion time and processing cost by analyzing the relationship among the global cooperation efficiency, redundant processing paths and the first-or der degree. Secondly, according to the strong local search ability of sparrow search algorithm, this paper designed a fusion strategy that based on nondominated sorting genetic algorithm and sparrow search algorithm, and an initial solution generation mechanism basing on clustering coefficient. Finally, the experiment illustrates the correlation between network characteristics and intercell scheduling objectives, so the proposed model and algorithm have higher quality of solution. [ABSTRACT FROM AUTHOR]

The uncertainty factors operation scheduling in the lead to inability to obtain accurate time of operation and ends under emergency rescue situation. In order to solve the problem, this paper proposed a grey scheduling model and a hybrid grey cuckoo algorithm. The model used three parameters interval grey number and the four parameters interval grey number to describe the uncertain time of operation and the uncertain ends. Then, it defined the possibility measure and necessity measure, proposed the index of delay confidence, which was used to estimate the probability of operation tardiness. After that, this paper established a gray mixed integer programming model with minimize average credibility of operation tardiness, and presented a solution method of the mixed grey cuckoo search algorithm. A classic example of size 6 ( 3) X 3 verified the proposed algorithm. The results show that the proposed algorithm can solve the problem very well, and the performance is better than the basic cuckoo algorithm. [ABSTRACT FROM AUTHOR]

To solve the problem of image threshold segmentation based on interval valued fuzzy sets, this paper proposed an threshold image segmentation algorithm using interval valued fuzzy set based on central disturbance . This algorithm used the way of disturbing to the center of object and background in an image, and considered the influence of uncertain and imprecise information on the center of clusters, and constructed interval valued fuzzy set for an image by using restricted equivalence function. Based on the distance measure between interval valued fuzzy sets, the algorithm searched the best segmentation threshold by establishing the objective function. Through the simulation experiment of three types of image data, the results show that the proposed method obtains better results in terms of vision and index, and proves the effectiveness of the 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]

In virtual resources scheduling of cloud manufacturing, algorithms were often inefficient; relational constraints between tasks, as well as logistics time and cost between tasks and sub-tasks, were not fully considered while modeling. In order to solve these problems, this paper constructed a multi-objective virtual resources scheduling model with the goal of minimizing the delivery time, minimizing the service cost and optimizing the quality of service. It used a double-stranded coding method based on project stage, and proposed an adaptive crossover and mutation probability formula, so as to avoid the low efficiency or premature convergence of algorithm caused by the invariance of crossover and mutation probability. On this basis, to guarantee the global and local search algorithm performance, it used an improved genetic algorithm combined with multiple crossover and mutation strategies based on project phase. The example shows that this model is more applicable than the traditional ones, and the improved genetic algorithm has considerable improvement in efficiency, accuracy and stability. [ABSTRACT FROM AUTHOR]