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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]

Aiming at the disadvantages of the HGS algorithm such as slow convergence and the tendency to fall into local optima, this paper presented a hunger games search algorithm based on equilibrium pooling and Levy flight. It employed tent mapping to generate a more diverse initial population. Inspired by the equilibrium optimiser algorithm ( EO), this paper proposed an update formulation based on the dynamic equilibrium pool convergence principle, whose dynamically tuned update strategy strengthened the global searching ability. To step out of the partial optimum furthermore, it performed a mutation operation based on Levy flight on population for certain condition. Simulation experiments were carried out for 23 benchmark functions. The results show that compared with original HGS algorithm, ELHGS has higher accuracy and converges more rapidly, which is obvious for high-dimensional multi-peaked functions. [ABSTRACT FROM AUTHOR]

As a stochastic search algorithm, the seagull optimization algorithm (SOA) has significant optimization performance, but it still has the problem of low population diversity and easy to fall into local optimum which resulting in low optimization accuracy. This paper proposes a improving seagull optimization algorithm combine multiple strategies (CMSOA) to improve the defects of the seagull optimization algorithm. Firstly, using the sine-cosine algorithm (SCA) to perturb and update the stagnant seagull population in the iterative process, which improved the diversity of the overall population; Secondly, introducing a scaling factor to adjust dynamically the relative displacement between the current seagull individual and the optimal individual, which improved the exploration and development ability of the algorithm; Finally, adopting Random opposition-based learning to fine-tune the optimal individual position of the seagull, which leaded the entire seagull move to the correct position in the given search space to improve the ability to jump out of the local optimum, and further increasing the optimization accuracy. In order to test the optimization performance of the improved CMSOA algorithm, this paper uses 14 CEC2017 test functions as the test benchmark to test the performance of CMSOA and others comparison algorithm. The experiments show that CMSOA has the advantage of optimization in the statistical sense based on the Freidman test. CMSOA has also achieved the best results in the path planning of 3 D UAV. [ABSTRACT FROM AUTHOR]

Aiming at the problems that the sparrow search algorithm (SSA) is prone to fall into local optimum and the optimization accuracy is low in the optimization process, this paper proposed a hybrid strategy improved sparrow search algorithm (MSSA). Firstly, in order to enable individual sparrows to fully search in the search space and increase the accuracy of algorithm optimization, it introduced an archiving stage in the algorithm optimization process to receive the residual position information that might be captured by sparrow producers when they moved to the safe area. Secondly, in the iterative process of the algorithm, it performed an adaptive neighborhood search operation on the current optimal individual, and fully explored the location information around the high-quality individual to enhance the algorithm's ability to jump out of the local optimum. This paper used 9 benchmark functions to evaluate the MSSA with SSA, and other four improved SSA, which were chaotic sparrow search optimization algorithm (CSSOA), mixed strategy improved sparrow search algorithm (MSSSA), improved sparrow search algorithm(ISSA), and enhanced sparrow search algorithm (ESSA). Experiment result illustrates that MSSA can achieve better convergence accuracy and stability on nearly 80% of the benchmark functions, and MSSA is also ranked first in the Friedman test. This paper also applied MSSA to optimize wireless sensor network( WSN) coverage in an obstacle environment. Compared with other five compared algorithms, MSSA can increase the coverage rates of WSN up 9. 77%, 4. 25%, 6. 62%, 3. 02%, and 7. 38%, respectively. [ABSTRACT FROM AUTHOR]

Increased number of Automated Guided Vehicles (AGVs) on automated container terminals leads to more frequent conflicts. This paper proposed an improved Conflict Based Search (CBS) algorithm. The bottom layer used the A* algorithm based on Manhattan distance, and the top layer combined the binary tree principle to build conflict trees to avoid conflicts between AGVs. With the objective of minimizing the total path length of AGVs between the shore bridge and the yard, the grid method used to developed the AGVs road network mode. Considering the point conflict and edge conflict among AGVs, the multi-AGVs conflict-free path planning problem in automated terminals statuted as a multi-agent path planning problem. The experimental results show that the proposed algorithm shortens the total path length and increases the computational speed with a guaranteed blockage rate of 0%, which verifies the effectiveness of the algorithm. [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]

Aiming at the shortcomings of the basic Gravity search algorithm（GSA）, such as prone to premature maturity, easy to fall into local optimum, and lack an effective acceleration mechanism, this paper proposed a GSA（IABHGSA） based on the improved adaptive black hole mechanism. The algorithm used the improved Tent mapping to initialize the population, which makes the distribution of the initial population more random, uniform and traversal, and it enhanced the global exploration capability of the algorithm; The algorithm introduced an improved adaptive black hole mechanism and selected the position update strategy, according to the evolution of the particles, which makes the position update more reasonable, Effectively reduce the possibility of particles falling into local optima; Through the optimal and worst particle update strategy based on learning ideas, enhanced the algorithm’s ability to escape from local optima and improved the algorithm’s optimization speed; The algorithm introduced group migration to provide effective accelerated convergence mechanism for the algorithm. Finally, this paper selected 8 benchmark functions to test IABHGSA and compared with the experimental results of related algorithms, the results show that IABHGSA has better optimization performance. [ABSTRACT FROM AUTHOR]

Classic flower pollination algorithm（FPA） can be easily exposed to the shortcomings of local optimal solution and slow convergence velocity. In view of these shortcomings, this paper proposed an FPA with an enhanced lens imaging strategy and random neighborhood-based mutation strategy. The lens imaging strategy can help the algorithm avoid the shortcoming of local optimal solution by expanding the search space of FPA to increase the diversity of the solution. The introduction of random neighborhood-based mutation strategy can enhance the convergence accuracy and search speed of the algorithm by guiding algorithm search with information in the neighborhood. A comparison of the improved FPA with four other improved algorithms on CEC2013 test function found that the improved multi-strategy FPA performs better than the comparison algorithms in both convergence accuracy and search speed. To study its practical utility, this paper applies the multi-strategy FPA into the automobile transmission parameter model and the results indicate that multi-strategy FPA is better than the comparison algorithm in optimization of automobile transmission parameters. [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]

Community search,as an important research content of social network analysis,aims to find densely connected subgraphs that highly relate to the query node given by users.Most community search methods currently available focus on homogeneous networks.However,in reality,information networks are often attribute-heterogeneous.This paper proposed P-distance and S-distance based on meta-path P and meta-structure S in heterogeneous networks,as well as (k,d,P) -truss and (k,d,S) -truss community models,to measure the structural cohesion of subgraphs.Additionally,it proposed a keyword attribute score function to measure the keyword attribute correlation of different subgraphs,and presented algorithms which could find communities with the highest keyword attribute score of (k,d,P) -truss and (k,d,S) -truss.Search algorithms could find a personalized community with both structural cohesion and keyword attribute correlation,and support to limit the maximum distance d between the query node and any node in the community to control the scope of community search.Compared with the related community search algorithms on real-world datasets,the experimental results prove the effectiveness and feasibility of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Most of the existing virtual network mapping algorithms rely on manual rules to sort nod es and determine the sequence of node mapping so as to optimize node mapping and improve the success rate of virtual network requests . In the link mapping stage, it generally uses the breadth-first search algorithm, ignoring the strong correlation between node resources and link resources, so that it can only obtain local optimal mapping results. In response to the above problems, based on the SG multi-domain heterogeneous network environment, from the perspective of network survivability protection, this paper proposed a virtual network mapping algorithm using two-layer reinforcement learning. It applied reinforcement learning to both the node and link stages of network mapping, used the gradient strategy and back propagation method to train the network model of this paper, and used the training model of this paper to complete the mapping. The simulation results show that, compared with the comparison algorithms, the algorithm optimizes the link mapping while optimizing the node mapping, and achieves better results in the mapping success rate, long-term return rate, and node and link utilization rate. [ABSTRACT FROM AUTHOR]

To solve the shortcoming of the meta-heuristic sparrow search algorithm, such as early convergence, easy to fall into local optimum, poor global searchability, this paper proposed a multi-strategy sparrow search algorithm integrating golden sine and curve adaptative. Firstly, it used Chebyshev chaotic mapping to initialize the population, so that the initial solution position distribution was more homogeneous, produced high-quality initial solutions, and increased the richness of the population. Secondly, it introduced golden sine and curve adaptive weight to improve the location update method of the discoverer and the joiner, which effectively coordinated global search and local mining capabilities and accelerated the convergence speed. Finally, it selected the random walk or Cauchy-t disturbance strategy disturbed the optimal sparrow position dynamically, which improved the ability of the algorithm to jump out of the local optimum and improved the convergence accuracy. This paper selected 14 benchmark functions for testing, compared the simulation results of the proposed algorithm with other 9 metaheuristic algorithms, and used Wilcoxon rank-sum test and MAE ranking to verify the effectiveness of the proposed improvement strategy. The results prove that the proposed algorithm has a great improvement in global searchability, overcoming local optima, convergence speed, convergence accuracy, and stability [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 order to improve the CSA with low speed of convergence and insufficient convergence accuracy. This paper proposed a MSCSA. Firstly, the algorithm introduced an adaptive weight coefficient with disturbance of tent sequence at early stage which could improve the convergence speed of the algorithm. Secondly, the algorithm introduced an operator which mixed by golden sine and moth-flame at late stage. At last, the algorithm improved the discovery probability AP which could improve the convergence accuracy of the algorithm by increasing the randomness. This paper determined the value of the iteration coefficient by comparing and testing on 9 benchmark functions. The paper verified the performance of the algorithm by the Wilcoxon rank sum test. The results of the test prove that the performance of MSCSA is better. [ABSTRACT FROM AUTHOR]

In order to provide decision support for vehicle scheduling of logistics companies, this paper investigated the routing problem with time windows and dynamic demands considering a mixed fleet of electric and conventional vehicles, and proposed a two-stage integer programming model to minimize the total distribution cost. This paper designed an improved adaptive large-scale neighborhood search algorithm(IALNS), proposed the new deletion and repair operators and acceleration strategy in the dynamic stages. It conducted the extensive large-scale computational experiments with both static and dynamic demands to examine the performance of proposed IALNS. The results show that, compared to IALNS-ND, IALNS performs better in term of the minimum and average values in 75% of the static problems ( 9 out of 12 cases) . In 95% (57 out of 60 examples) of the dynamic cases, IALNS works better than IALNS-ND in terms of the cost and computation time . Moreover, compared to ALNS, LNS and VNS, IALNS performs best in term of the best minimum and average values of the total costs for all static cases. In 58% (35 out of 60 examples) of the dynamic case, the IALNS can achieve a better solution in 1.5 times or even 10 times less computation time than the rest algorithms. Also the larger the degree of dynamism of a experiment is, the better the obtained solution obtained by IALNS in a shorter time. Thus IALNS performs best in solving the time-sensitive dynamic demand vehicle routing problem. [ABSTRACT FROM AUTHOR]

In order to improve the exploration efficiency of the search-type path planning algorithm in the C-shaped obstacles, this paper proposed an A* algorithm based on the generative adversarial network. First, the method used a more stable Wasserstein generative adversarial network with gradient penalty (WGAN-GP) to generate regions of interest with a feasible path. Then the algorithm used the A* algorithm to preferentially explore the regions, enabling it to efficiently guide path planning. Finally, it formed a continuous path. According to the experimental simulation verification, compared with the traditional A* algorithm, it saved 31% less planning time and 22.84% less exploration space, and improved the efficiency of the path planning algorithm. Experimental results show that the improved A * algorithm has high exploration efficiency, and it can achieve better application in robot path planning. [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]

MSV is one of the effective methods to reduce SoC power consumption. In order to facilitate the placement of power supply pins of each voltage island, this paper proposed a multi-voltage floor planning algorithm considering voltage island boundary constraints. Firstly, based on the representation characteristics of slicing tree, this paper proposed a boundary checking algorithm to quickly determine the boundary information of all modules. Then, in order to minimize the power consumption, this paper used the improved dynamic programming method to assign voltage to each module and construct voltage islands. Finally, it took simulated annealing algorithm as search engine to optimize the chip area, wire length and power consumption simultaneously. In addition, to reduce iteration times, the SA algorithm adopted a two-stage cooling strategy. Experimental results on GSRC circuits show that the proposed algorithm can generate multi-voltage floorplan satisfying boundary constraints, and the power consumption increases only by 5.2% on average compared with that without considering boundary constraints. [ABSTRACT FROM AUTHOR]

In the simultaneous pickup and delivery problem, every customer has both delivery demand and pick-up demand, and both demands need to be served simultaneously. Under this condition, this paper established a location-routing problem with simultaneous pickup and delivery model to minimize the sum of location cost, routing cost and transportation cost. For solving this model, this paper developed a HIA. It generated the initial solution by greedy clustering algorithm, and evaluated and sorted the antibody by the original immune algorithm. And it improved the immune operation of the original algorithm by the neighborhood search operation. Finally, this paper verified the feasibility of the model and the effectiveness of the algorithm by using the HIA, the original immune algorithm, the simulated annealing algorithm and the ant colony algorithm. . [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]

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]

This paper proposed an optimized mapping algorithm for service function chain based on improved sparrow search algorithm to realize the low-cost and efficient deployment of service chain in 5G network scenario. It coded the composition schemes and the mapping schemes of service requests together by a double-layer coding method in the mapping process, and then used an improved sparrow search algorithm to obtain the service function chain deployment scheme of the request and calculated the mapping weight. When there were multiple request types in the same time slice, it sorted all service function chains according to the mapping weight,and mapped the requests that had high weight as priority,so as to reduce the delay overhead and improve the utilization of node computing resources and link resources. Simulation results show that the algorithm can effectively reduce deployment overhead and improve resource utilization. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of COA, such as slow convergence speed, low solution accuracy and being easy to fall into local optimum, this paper proposed an improved coyote optimization algorithm based on dual strategy learning mechanism and adaptive chaotic mutation strategy ( DCSCOA ). Firstly, it adopted an oscillatory decline factor to generate diverse individuals for enhancing the global search ability. Secondly, it proposed a dual strategy learning mechanism to appropriately increase the influence of the group head wolf, so as to balance the local search ability and global search ability of the algorithm, and to improve the solution accuracy and convergence speed of the algorithm. Finally,it used an adaptive chaotic mutation mechanism to generate new individuals when the algorithm stagnates, so as to make the algorithm jump out of the local optimum. Through simulation experiments on 20 basic test functions and 11 CEC2017 test functions, the results show that the improved algorithm has higher solution accuracy,faster convergence speed and stronger stability. [ABSTRACT FROM AUTHOR]

Aiming at the characteristics of the traditional cuckoo search algorithm with low convergence accuracy and large limitation of iterative step for complex problems, this paper proposed a dynamic cuckoo search strategy based on rider optimization. The algorithm combined with the idea of rider optimization algorithm, using multiple groups and optimal strategies in a single cycle . The optimal strategy could dynamically enhance the searching and improve the convergence efficiency of the algorithm for complex problems. At the same time, this algorithm adjusted the dynamic parameters of Levy flight motion to improve the early and late performance of the algorithm search. The simulation test results show that the optimization performance of the imp roved algorithm ROCS is better than the comparison algorithms for complex problems, and the optimization efficiency of the algorithm has been significantly improved. [ABSTRACT FROM AUTHOR]

In order to use Harris hawks optimization ( HHO) to solve KPC, this paper proposed a new discrete Harris hawks optimization ( DisHHO ) algorithm. DisHHO encoded individual based on 0-1 vector and established the discrete evolution equations by bitwise operation. Furthermore, it used an adaptive mutation mechanism to improve the search performance. For verifying the performance of DisHHO in solving KPC, it was used to solve four kinds of large -scale KPC instances. The comparison with the existing binary HHO and state-of-the-art algorithms for solving KPC show that DisHHO not only has excellent average calculation results, but also has fast calculation speed. Hence, DisHHO is a new and efficient algorithm for solving KPC. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of normal SPBO, this paper analyzed the psychological characteristics of students ' learning, and put forward an improved student psychology based optimization algorithm ( HSSPBO) using hybrid strategy . Firstly, it took the inverse value of student ' s test scores as the fitness value of the student, and took the fitness value of the best students in the class as the benchmark, and then divided the students in the class into four categories : the best students, the good students, the ordinary students and the students who tried random improvement. Secondly, it used the dynamic switching probability of sine square and cosine square to balance global exploration and local development, so that the global exploration ability and local exploitation ability of the algorithm were improved effectively. Thirdly, by introducing Cauchy mutation strategy to change the local search step size, it improved effectively the local search ability of the algorithm and enhanced the ability of the algorithm to jump out of the local optimization. Finally, the HSSPBO algorithm used Levy flight strategy to make the individual search step length more random and flexible, which effectively enhanced the individual ' s searching ability, and further improved the optimization speed of the algorithm. Through simulation experiments of 12 benchmark functions and comparison with six optimization algorithms, the results show that HSSPBO ' s global search ability has been significantly improved, and it has faster global convergence speed, better optimization accuracy and stability in function optimization. [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]

Aiming at the shortcomings of Harris eagle algorithm (HHO) in solving Job-Shop scheduling problem (JSP), such as poor optimization ability and easy to fall into local optimization, this paper proposed a hybrid Harris eagle algorithm (HHHO). Firstly, the algorithm used chaos theory to increase population diversity in the population initialization stage. Secondly, in the early stage of HHO search, the algorithm used nonlinear energy decline and quantum computing to enhance the global exploration ability of the algorithm. In the later stage of search, the algorithm adopted neighborhood search algorithm to enhance the local development ability of the algorithm. Finally, the algorithm selected FT and LA basic examples to test the performance of the algorithm, and compared with other advanced meta heuristic algorithms to verify the effectiveness and superiority of HHHO in solving JSP. [ABSTRACT FROM AUTHOR]

Internet is one of the important network carriers of the industrial Internet of Things. Because of its dynamic changes, the path will expire when supporting the transfer of group control commands, which cannot achieves collaborative manufacturing to equipments scattered in different regions. This paper transformed the group control command transfer problem into constrained dynamic multi-Steiner trees problem firstly, and proposed a kind of improved dynamic genetic algorithm. The algorithm proposed a hybrid search strategy with static and dynamic dimensions. It executed static search strategy when network state didn' t change, static search strategy mainly considered alleviating premature phenomena. It executed dynamic search strategy when network state changed, dynamic search strategy mainly considered improving convergence and using static search strategy to alleviate premature phenomena. This paper verified the validity, convergence and global search ability of the algorithm from transfer success rate, convergence delay rate. [ABSTRACT FROM AUTHOR]

Aiming at the problem of low efficiency of rubber mark detection due to the diversity of morphological features of airport runway rubber mark and the complexity of the runway environment, this paper proposed a new airport runway rubber mark image segmentation algorithm based on simplified pulse coupled neural network. Firstly, on the basis of optimizing feedback input by using neighborhood gray-scale relationship, this algorithm improved the neuron ignition threshold mechanism from the traditional exponential decay to linear stratified step decay. Then, it combined the cuckoo search algorithm with the minimum cross entropy to optimize the parameters of the improved model. Finally, according to the average gray value of the ignition map ping area, it constructed an adaptive iterative termination condition. The experimental results show that the proposed algorithm has higher rubber mark detection accuracy and better anti-interference performance under the influence of poor lighting conditions and marking lines. [ABSTRACT FROM AUTHOR]

To solve the disadvantages of the most PV models parameter identification algorithms at present, which have low accuracy and poor reliability, this paper proposed an improved symbiotic organisms search ( SOS) algorithm for parameter identification of PV module models. First, to enhance the performance of original SOS, this paper proposed a novel improved SOS algorithm, named as ImSOS. In ImSOS, it employed a quasi-reflection-based learning( QRBL) scheme in the population initialization step of original SOS. Moreover, it used the strategy of the modifications of benefit factors in the mutualism phase of SOS. It adopted a strategy of narrowing the search range of randomly generated coefficients in the commensalism phase of SOS. And then, it detailed the procedures and flowchart of employing the ImSOS for solving the PV module models parameter identification problem based on experimental current versus voltage(I-V) data of a real PV module. Finally,the ImSOS was demonstrated on the parameter identification of different PV module models of the Sharp ND-R250A5 PV module. Experimental results and comparisons with original SOS and the other seven novel intelligent optimization algorithms implies the effectiveness and superiority of the ImSOS. Therefore, the ImSOS becomes a new effective method to accurately and reliably identify PV module models parameters. [ABSTRACT FROM AUTHOR]

The traditional housing prices evaluation which is using random forest algorithm has a large number of parameter selection problems and the parameters has great influence on the accuracy of the algorithm. In order to solve the above problem, this paper proposed a new algorithm combined the random forest algorithm and simulated annealing algorithm about the housing prices evaluation. Firstly, this paper tested the sensitivity of parameters by 10 times 10-cross-validation method and selected the parameters which were sensitive of the random forest algorithm. Secondly, it used the simulat ed annealing algorithm to optimize the sensitive parameters iteratively. Through comparing t o the grid searc h algorithm and ra ndom search algorithm, it found the simulated annealing algorithm was better in th e running time a nd algorithm accuracy, whic h made up the d efects of th e time-consuming and the l ow-accuracy of the random search algorithm in the grid searc h when sel e cting para met e rs. At l ast, this paper applied th e random forest algorithm combing with simulated annealing to the housing prices evaluation, and formed a new evalu-ation algorithm. Compared with the traditional random forest price estimation algorithms, the results show that the proposed al-gorithm reduces the error value, inc reases the goodness of fit value, a nd improves markedly the accuracy of the evaluation. [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]

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]

K-ary search tree is a promising structure due to its friendliness to cache awareness and SIMD acceleration. Recent years have witnessed its successful application to inverted index for efficient query processing and index compression. However, its query processing methods are either complicated or rudimentary, and its adoption is limited in membership test, which both impair its application in real life. This paper studied the intersection algorithm for k-ary search tree, and introduced two early termination techniques collaborating with particular traversal methods to improve its intersection efficiency. The experimental analyses validated the proposed optimizations could greatly improve the query speed, and the optimized k-ary search tree suits better for long inverted lists, and when equipped with larger SIMD registers, it is able to beat the state-of-the-art sorted-array based intersection algorithms. [ABSTRACT FROM AUTHOR]

The simulated annealing algorithm with adaptive temperature rising strategy or Monte Carlo strategy has the problems of slow convergence and limited global optimal approximation ability in solving complex 'ISP respectively. On the other hand, the existing chaotic optimization algorithm has the defect of logistic mapping, which weakens its ability to jump out of local optima. Therefore, this paper designed a fusion algorithm framework, in which embedded the divided Lorenz chaotic mapping system to enhance the search efficiency of the chaotic algorithm for the neighborhood solution. It introduced the greedy strategy to construct the initial solution approaching the global optimal solution, which made the algorithm had the ability of transition to the neighborhood of the global optimal solution. In addition, it designed the complementary mechanism of oscillatory annealing to improve the sub iterative solution screening process, and enhanced the global search performance of the algorithm. After the implementation of the algorithm, using the international public 1SPLIB point set, through multiple rounds of comparative testing, it verifies that the performance of the new algorithm is better than the comparison group simulated annealing algorithm and logistic chaos optimization algorithm,and has shorter convergence time and stronger global optimal approximation ability. [ABSTRACT FROM AUTHOR]

As many intelligent devices become the component of the Internet of Things system, the system' s architectural form and scale are showing diversified and huge characteristics . The performance of an IoT system depends on its own organizational structure . Therefore, the optimization of the organizational structure of the Internet of Things system is particularly important. Because the essence of the system architecture is the problem of the arrangement and combination of system nodes, this paper converted the system architecture problem into the optimization problem of the system utility value, and proposed a solution algorithm based on immune evolution. The algorithm used a hierarchical coding method to transform the system organization structure into an integer coding form. It designed an adaptive hierarchical crossover operator and as mall perturbation mutation operator, and used the degree of individual stimulation to guide the search direction of the algorithm and search for the algorithm that met the performance requirements. Experimental results show that this algorithm can obtain a system structure with high efficiency, and has excellent performance in success rate, relative error rate and convergence speed. [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]

The traditional fireworks algorithm has slow convergence speed and low precision when solving large-scale discrete problems. Aiming at the characteristics of travelling salesman problem, this paper proposed a discrete fireworks algorithm with fixed radius nearest-neighbor search 3 -opt. It discretized and improved the proposed algorithm based on the basic fireworks algorithm, where adopted the integer coding path representation method to represent the solution of the travelling salesman problem, as well as designed the explosion operator and the Gauss mutation operator of the algorithm for discretization solution. To enhance local search ability of the algorithm, the algorithm proposed implements 3-opt local search to strengthen search ability of the algorithm, and introduced the fixed radius of neighbor search and don't-look-bits strategy to improve the efficiency of the algorithm. The experiments indicate that the proposed algorithm solves travelling salesman problem effectively . The discrete fireworks operator proposed is superior to the traditional fireworks operator in global convergence ability, convergence accuracy, solution time and stability . The average costs of the solutions of the algorithms for all benchmark instances probed have an overall deviation in only 0. 002% from those of the best known solutions, which is lower than those of all comparing algorithms. [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]

Butterfly optimization algorithm is a newly natural heuristic algorithm, which simulates the butterfly's behavior of using smell to determine the location of nectar or mating object. To solve the problem that the butterfly optimization algorithm was prone to low accuracy and slow convergence, this paper proposed the crazy butterfly optimization algorithm integrating with adaptive perturbation (CIBOA). Firstly, it introduced the adaptive inertial weights to the self-cognitive flight part to balance the local and global search capabilities of the algorithm. Secondly, it introduced the perturbation strategy to the global optimal position to avoid the algorithm falling into the local optimal. Finally, it introduced the crazy operator to the nectar location to increase the population diversity and obtain better optimal solution. To compare the search performance of five algorithms in 10, 30 and 50 dimensions used eight benchmark functions. The experimental simulation results show that the comprehensive performance of the improved algorithm is better than other algorithms. [ABSTRACT FROM AUTHOR]

The existing heuristic algorithm has low efficiency in the DEM path planning because of the large amount of data. In order to solve this problem, this paper proposed a hybrid path planning algorithm based on genetic algorithm and ant colony algorithm. In the genetic process, the algorithm constructed the selection factor in the initial population generation stage, which made the searched node more incline to the end direction and improved the efficiency of the initial population generation. And the algorithm restricted the variation interval of the mutation node during the mutation process to avoid the path break point. In the ant colony optimization process, according to the path information generated by the genetic process, it adopted the adaptive pheromone initialization and updated strategies to improve the algorithm search efficiency. Simulation experiments show that the hybrid algorithm can search the qualified path efficiently under the rule grid DEM. [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]

Aimed at the problems of hyperspectral images unmixing, it is found that the unmixing accuracy of the traditional unmixing algorithm is not high when the number of endmember keeps constant in unmixing. Thus, based on the ANN, this paper proposed a novel unmixing algorithm of estimating the number and category of endmember in a single pixel. Firstly, the unmixing algorithm used the ANN to estimate the number and category of each mixed pixel' s endmember in the remote sensing image. Then, it determined the objective function of the algorithm based on the estimation results, and introduced the improved differential search algorithm to solve the objective function. Finally, it obtained the abundances and the parameters to realize the unmixing of hyperspectral images. The experimental results on simulated and real hyperspectral data demonstrate that compared with the existing unmixing algorithms, the proposed unmixing algorithm has higher performance and is more in line with the actual scene . [ABSTRACT FROM AUTHOR]

According to the slow convergence speed and low searching precision of traditional backtracking search optimization algorithm, this paper proposed an improved algorithm based on cellular automaton and orthogonal experimental design. Firstly, the algorithm intrcxluced orthogonal experimental design method into the crossover operator to obtain representative high-quality offspring individuals. Then based on the neighbor mcxlel of cellular automaton, it carried out the orthogonal crossover operation of multiple parents in the domain for individuals, which was beneficial to improve the mining capacity and search efficiency of the algorithm. Finally, in order to balance the global searching and local searching ability of the algorithm, it introduced the dynamic proportional weight of excellent individuals into the cross-population to select and update them, with adopted a new dynamic variation equation. The simulation experiments selected 12 standard test functions and compared with 6 other well-behaved algorithms, the results show that the improved algorithm has obvious advantages in convergence speed and optimization accuracy. [ABSTRACT FROM AUTHOR]