Search

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 improve the shortcomings of the aquila optimizer (AO) in solving complex optimization problems, such as easily falling into local optimality, this paper proposed a multi-strategy improved aquila optimizer (MIAO ) . Firstly, this paper proposed the mirror simplex method strategy to expand the search range of aquila optimizer, enhancing the population diversity and the ability to escape from shackles. Secondly, the X3 phase of the aquila optimizer combined the social free foraging strategy to escape from the bondage of global mean and improved the diversity of individual aquatic eagles in the late iteration. At the same time, the X4 phase incorporated the ladder stepping strategy to ensure that the current dominant individuals accelerated the trend toward the global optimum and increased the convergence speed. Finally, it enhanced the optimization ability of the algorithm by improving the original development mechanism. Experimental results and Wilcoxon signed rank sum test results of 10 commonly used benchmark functions and some of the CEC2017 functions show that the improved algorithm has better performance in terms of accuracy, convergence and stability. In addition, through testing and analyzing a mechanical optimization design experiment to further verify the superiority and practicability of the improved algorithm. [ABSTRACT FROM AUTHOR]

When the meta-heuristic algorithm solves the joint resource allocation problem of multi-beam satellites, the computational complexity increases and the algorithm is difficult to converge due to the time delay constraint and capacity constraint. This paper introduced a penalty mechanism in the objective function, and added a penalty value to the objective function of the invalid solution, so that the optimized solution adaptively satisfied these two constraints. Based on this, this paper proposed a joint resource allocation algorithm based on quantum-behaved particle swarm optimization. Simulation results show that the introduction of the penalty strategy solves the problem of difficulty in handling the delay constraint and capacity constraint when applying the meta-heuristic algorithm. The quantum-behaved particle swarm optimization algorithm with penalty mechanism outperforms the existing joint allocation algorithm in terms of allocation fairness index and total system capacity. [ABSTRACT FROM AUTHOR]

To improve the local mining and global search ability of quantum-behaved particle swarm optimization algorithm ( QPSO), this paper proposed an improved QPSO algorithm( DSQPSO). DSQPSO algorithm introduced the double strategies co-evolution to adjust the particle position update formula. Firstly, in order to fully reflect the advantage of individual exploration and the characteristic of collective guidance, this paper put forward two kinds of ideas of attraction points to achieve better integration of individuals and the swarm as well as information exchange. Secondly, it redefined the search scope of the particle through considering the relationship between the optimal average position and the global optimum and individual's historical optimum respectively. Moreover, in the iterative process, DSQPSO used the random perturbation mechanism to adjust the global optimal position in order to help the diversity of the swarm to be preserved. Based on 18 test functions, this paper compared DSQPSO with PSO, QPSO, RQPSO and LQPSO in convergence accuracy and robustness. Furthermore, in terms of the optimization results, it compared the improved algorithm with eight intelligent algorithms on two practical engineering optimization problems. Experiments indicate that whether in benchmarking or in engineering application, DSQPSO has obvious advantages in calculation precision and convergence effect. [ABSTRACT FROM AUTHOR]

To solve the problems of low convergence accuracy, slow convergence speed and easy to fall into local optimum of traditional particle swarm algorithm, this paper proposed an improved particle swarm algorithm with multistrategy fusion. Firstly, in order to accelerate the convergence speed of free particles, the improved algorithm used a method of updating the position of free particles based on the midperpendicular algorithm. secondly, the improved algorithm designed a strategy of generating exploding particles near the optimal particles to enhance the optimizationseeking accuracy and optimization-seeking speed of the algorithm, and the improved algorithm also designed a particle velocity updating strategy relying only on the global optimal particle position to accommodate the first two strategies. Finally, The algorithm also used the inertia weights and particle position update methods of the simplified particle swarm optimization algorithm based on probabilistic hierarchy. This paper designed a few comparison experiments with other five improved particle swarm algorithms, and the results show that the improved algorithm in this paper has a greater advantage and better performance whether dealing with low-dimensional problems or high-dimensional problems. [ABSTRACT FROM AUTHOR]

When UAVs are equipped with a deep neural network for power inspection, because the device computing power and battery capacity as well as the deep neural network computation load limit, it cannot independently process the massive amount of image data generated in the inspection task. To address this problem, this paper proposed an unloading strategy for UAV power inspection based on improved hybrid particle swarm algorithm and matching theory, which decomposed the system cost minimization problem into two sub-problems, such as cooperative partitioning of deep neural network computing tasks and edge server selection. For the collaborative partition problem, it proposed a staggered transmission method based on the execution process of deep neural network computational tasks, and an improved hybrid particle swarm algorithm solved the multi-UAV task collaborative partition layer. For the server selection problem, it defined the respective preference functions of the UAV and the server, and established a stable match between them through matching theory according to the preference functions to obtain the server selection policy. Simulation results show that the proposed strategy can effectively reduce the energy consumption and computational task processing delay of UAVs and promote the load balancing of edge servers compared with other offloading strategies. [ABSTRACT FROM AUTHOR]

Aiming at the shortcoming of salp swarm optimization algorithm such as low accuracy, slow convergence speed and easy to fall into local optimum, this paper proposed an adaptive t-distribution and dynamic weight salp swarm optimization algorithm. Firstly, the leader position update introduced the global search stage formula of butterfly optimization algorithm to enhance the global exploration ability. Secondly, the follower location update introduced an adaptive dynamic weighting factor to strengthen the guiding role of elite individuals, so as to enhance the local development ability. Finally, the adaptive t-distribution mutation strategy mutated the optimal individual in order to avoid the algorithm falling into local optimum. By solving 12 benchmark test functions, and according to comparison results of the mean value, standard deviation, solving success rate, Wilcoxon test and convergence curve, the proposed algorithm was superior to standard salp swarm algorithm, the compared other improved salp swarm algorithm and the compared other swarm intelligence algorithms. The results also show that it has a significant improvement in the optimization accuracy and convergence speed, and has the ability to jump out of local optimum. The experimental results verify the effectiveness of the proposed algorithm by applying it to find the lowest point of denitrification inlet concentration. [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]

The basic seagull optimization algorithm(SOA) has some drawbacks such as low solution accuracy, slow convergence rate, and easy to fall into local optima when solving complex function optimization problems. This paper proposes an improve SOA based on somersault foraging strategy(abbreviated as SFSOA). First, the proposed SFSOA updates the positions of seagull individuals by using nonlinearly decrease strategy of control parameter A based on the inverted sigmoid function. This way improves individual solution quality and accelerates convergence speed of algorithm. Then, a novel learning mechanism based on the somersault foraging strategy is introduced for the global best individual to increase the position diversity of seagull individual and avoid the algorithm falling into a local optimum at the lately stage. The proposed SFSOA is compared against the basic SOA, grey wolf optimizer(GWO), and improved SOA(ISOA) by using eight complex benchmark function optimization problems. Experimental results reveal that the proposed SFSOA has higher solution accuracy, faster convergence rate and stronger global search ability than its competitors and can effectively deal with complex function optimization problems. Finally, SFSOA is applied to solve the feature selection problems and the satisfactory results are obtained. [ABSTRACT FROM AUTHOR]

Disaster preparedness measures can prepare for disaster relief to ensure that emergency supplies can reach the disaster area in a timely and effective manner. This paper proposed a bi-level programming emergency resource scheduling location-routing model with considering preparedness ( BLERSLR-P) In the model, the upper level aimed to minimize the total cost of supply station construction and operation, while the lower level aimed to minimize the cost of distribution route. This paper designed an improved bi-level salp swarm genetic algorithm (BLSSAGA) to solve this problem, combined the concept of iterative division to update the leader position, used the natural exponential inertia weight strategy to modify the control factor, used the chaotic map to update the follower position, and used Taguchi analysis to obtain reasonable values of parameters. Finally, this paper used bi-level salp swarm genetic algorithm ( BLSSAGA) and genetic particle swarm optimization algorithm ( GAPSO), particle swarm optimization algorithm( PSO), and immune optimization algorithm(IA ) to solve and compare and analyze the LRP data set in OR-Library, which verified the feasibility and effectiveness of the proposed model and algorithm. [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 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 problem of "over-exploitation" in dimensional learning strategy (DLS), this paper proposed a multiswarm collaborative particle swarm optimization algorithm with comprehensive dimension learning (CDL-MCPSO). The cluster structure of master-slave paradigm divides the population into a master group and four slave groups, in which the master group executes a comprehensive learning strategy to conduct a large-scale exploration in the search space, and the slave groups execute a comprehensive dimensional learning strategy (CDL) to exploit high-precision solutions near the local optimal. The master-slave groups can effectively achieve the balance between exploration and exploitation by executing algorithms with different functions. At the same time, in order to maintain the diversity of the population, the CDL-MCPSO proposed a new solution exchange mechanism (SEM), By doing this, the algorithm can accomplish information exchange and cooperation after the master-slave groups running their respective algorithms for several generations independently, so as to guide the particles to conduct more accurate searches in the later stage. Finally, for the high randomness of the initialization process, the algorithm adopted latin hypercube sampling to reconstruct the input distribution. In order to verify the effectiveness of CDL-MCPSO, compared with 5 kinds of PSO variants in 10 test functions. the results show that the algorithm can always find better or equivalent solutions. It is feasible and efficient in solving complex functions. [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 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]

Aiming at the problem of blank and overlapping coverage in node deployment of wireless sensor network, this paper proposed a network coverage optimization based on improved equilibrium optimizer (IEO) algorithm. Firstly, it used surround reverse learning to improve the quality of initial population and enhance the optimization ability of the algorithm. Secondly, it introduced dynamic sine and cosine factors to further balance global search and local development ability, so that particle population could search extensively and dig deeply in the search space. Finally, it added circle chaos map in the concentration updating stage to increase the diversity of population and improve the algorithm's ability to escape local optimum. The experimental results show that the IEO algorithm applied to the coverage optimization experiment of WSN can effectively reduce the deployment cost, show higher network coverage and improve the monitoring quality of the network compared with the standard balance optimizer algorithm and other improved algorithms. [ABSTRACT FROM AUTHOR]

To solve the RFID network planning problem of flexible manufacturing workshops with multiple coverage requirements, this paper proposed an RFID network planning approach integrating hierarchical clustering, redundancy reduction, and gradient descent, where the deployment cost, reader interference, and reader efficiency were taken as multi-optimization objectives. Firstly, it designed a hierarchical clustering algorithm to determine the initial number and deployment location of RFID. Then, it constructed a redundancy reader reduction algorithm to optimize the number of RFID. Finally, it built a gradient descent algorithm to adjust the RFID deployment location, so as to achieve multi-objective optimization of the RFID network planning. Experimental results show that the proposed approach has better multi-objective comprehensive performance than the traditional RFID network planning methods, including the hierarchical clustering method, genetic algorithm, particle swarm optimization, and redundancy subtraction method. It means the proposed approach is effective. [ABSTRACT FROM AUTHOR]

In order to solve the problems of the MBO algorithm, such as it is easy to fall into local optimum and the convrgence speed is low, this paper proposed an improved MBO algorithm with CSMBO. Firstly, it introduced a dimension based vertical crossover operation to substitute the original migration operation of MBO, and then generated a cross migration operator. Thus, this operation could improve search ability of MBO algorithm effectively. Secondly, in order to speed up the convergence of MBO algorithm, the sharing adjustment operator with information sharing replaced the original adjustment operator. Finally, this paper utilized the greedy strategy to instead of the elite strategy of MBO, which could reduce one sorting operation and improve the calculation efficiency of MBO algorithm. To evaluate the optimization ability of this CSMBO algorithm, this paper made some experiments on a set of common benchmark functions with 30-dimensions and 50 dimensions . The results show that the proposed CSMBO algorithm has good optimization performance, and outperforms currently available three optimization approaches. [ABSTRACT FROM AUTHOR]

In order to solve the high transportation cost problem faced by the third party logistics (3PL) suppliers, from the fourth party logistics ( 4PL) supplier' s point of view, this paper proposed a multi 3PL collaborative routing customization problem. As the problem needed to consider the route and the 3PL simultaneously, this paper designed a hybrid particle swarm optimization based K-shortest path algorithm ( K-PSO). In the experimental analysis, the results show the effectiveness of the K-PSO by comparing with the GA and enumeration algorithm with different nodes' and 3PL suppliers' number. Finally, through the changes of the 3PL suppliers' transit cost, it shows the advantages of the 4PL collaborative transportation. [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]

Butterfly optimization algorithm (BOA) is a novel nature-inspired metaheuristics algorithm proposed in recent years. The basic BOA is slow convergence, low accuracy and easy to fall into local optimum. To solve the above problem of basic BOA, this paper proposed an adaptive butterfly optimization algorithm based on mutation strategies ( ABO A-MS) . Firstly, it introduced the strategy of adjust the conversion probability dynamically, which effectively balanced the ability of the global exploration and local search, by means of dynamically adjusting the switching probability of the change information of iteration times and individual fitness. Secondly, it introduced the adaptive inertia weight strategy and local mutation strategy. It applied the inertia weight value and chaotic memory weight factor, which further improved the diversity of this algorithm, and effectively avoided its precocious convergence, as well as accelerated its convergence speed and solving accuracy. In order to verify the optimization performance of the modified algorithm, it carried out the simulation experiments among the modified algorithm, the basic BOA algorithm, the particle swarm optimization algorithm, the salp swarm algorithm, the gray wolf optimization and the others. Simulation results illustrate that the modified algorithm has excellent performance of fast convergence speed, high optimization accuracy and strong stability. [ABSTRACT FROM AUTHOR]

For the problem of multi-objective optimization, this paper proposed a multi-objective particle swarm optimization algorithm based on quasi-circular mapping（qMOPSO). Firstly, qMOPSO projected the solutions in high-dimensional objective space into the divided 2-dimensional quasi-circular space in order to monitor evolutionary status of the particle population. Then, the algorithm adopted quasi-circular dominant and individual distance based on sector blocks to design an archive maintaining algorithm which could balance diversity and convergence of solutions in the archive. In addition, it introduced an adaptive global best selection mechanism based on the solution distribution entropy to analyze the evolutionary tendency. Finally, the algorithm used a general management strategy based on changing dimension and elimination mechanism to enhance population management.qMOPSO was compared with five multi-objective optimization algorithms on three kinds of test suites. The simulation results show the correctness and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper proposed improved particle swarm optimization algorithm( GB_PSO ) for robot path planning to address the problems of easy to fall into local misconceptions when solving robot path planning by a single intelligent optimization algorithm. The algorithm took on the PSO as the main body,due to the GA and the BFO algorithm update strategies was subject to different environmental influences, fitting two environmental parameters. Then the correlation between particles and different environmental parameters calculated to classify the particle swarm into two classes, and local optimization enhanced in parallel by selection, crossover, and variation operators of GA and convergence operations of BFO, respectively . Finally the improved p article swarm update formulation used to achieve the optimization of global and local paths of the robot. The experimental results show that the GB_PSO algorithm for path planning improves the local and overall search capability. The path planning is fast and short distance, while having stronger robustness. [ABSTRACT FROM AUTHOR]

Aiming at the kinematics of single-segment and multi-segment continuum robots, this paper proposed a complete forward and inverse kinematics analysis method combining segmented constant curvature and particle swarm algorithm. Taking the double-segment wire-driven continuum robot as the research object, it designed the robot prototype with translation segment. Then it established the mutual mapping between the driving space and the joint space using the piecewise constant curvature method, and obtained the positive mapping relationship from the joint space to the working space according to the homogeneous transformation. Finally, it used the linearly decreasing weight particle swarm algorithm to realize the inverse mapping from the workspace to the joint space . The kinematics of the two-segment continuum robot was simulated and the time-consuming test of the inverse kinematics solution was carried out, and carried out the experimental verification on the developed prototype. The simulation results show the rationality of the proposed kinematics research method and the rapidity of the inverse kinematics solution. The experimental results show that the average position error is less than 6. 22% of the length of the two-segment continuum robot body, which verifies the effectiveness of the proposed kinematics. [ABSTRACT FROM AUTHOR]

This paper proposed an improved salp swarm algorithm that aimed to solve the problem of low precision and slow convergence speed in solving function optimization problems. For leaders, this algorithm replaced the optimal individual position with weighted centroid to prevent premature aggregation near the optimal individual. It also introduced adaptive inertia weight to the followers to balance the global search and local optimization capabilities of the algorithm. Furthermore, this algorithm contributed to reducing the inter-dimensional interference and improving the diversity of the population by dimensional random difference mutation. The simulation results illustrate that the improved algorithm is better than the standard tympanum group algorithm and other improved algorithms in terms of mean, standard deviation and convergence curve. The results also show that the improved algorithm improves the performance of optimization, and has higher precision as well as faster 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]

To enhance the correctness of the path planning of underwater swarm robots based genetic algorithm, this paper used theorem proving to formalize it, and gave the formal model of it in HOL4. According to the general steps of algorithm formalization, this paper analyzed the design procedure of the algorithm in detail firstly. It pointed out the key step of the algorithm and the difficulty in formal modeling, and constructed the overall frame of formal modeling. Then, it got three core modules of population initializing, selection and crossing by decomposing the algorithm. Finally, it formalized the three core modules separately after formalized the basic data types, which would be used in formal model, and got the formal model of the algorithm. By proving the 97 properties related to this model, this paper illustrates the rationality and effectiveness. Moreover, it carried out the formal verification of the algorithm, and also extended the application scope of HOL4. [ABSTRACT FROM AUTHOR]

In view of the lack of abundant sample data and sample unbalanced problem in equipment health prognosis, this paper proposed a health prognosis model based on IPSO -BSVM. Firstly, it put forward the dynamic non-linear inertia weight to improve the PSO algorithm. Secondly, it introdu ced the balanced SVM to improve the performance of SVM. Thirdly, it applied the improved PSO algorithm to optimize the parameters of the balanced SVM. Finally, it used the IPSO-BSVM model for equipment state identification and residual useful life prognosis. The simulation results show that the proposed method can effectively solve the problem of equipment health prognos is under small and imbalanced sample data. [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]

Aiming at the problem of attackers recognized false noise when using them to deal with statistical attacks, with the study of differential privacy protection model, this paper proposed a privacy protection method based on particle clustering and geographic location indistinguishable through real user location offset. This method could satisfy the basic requirements of the generalized differential privacy model by the way that the anonymous users in the current region, so the locations were offset respectively according to the way of particle clustering and realized the indistinguishable of geographical location. Finally, by comparing the simulation experiment with similar algorithms satisfying the generalized differential privacy models, it further confirmed the implementation efficiency and privacy security of the proposed algorithm. Therefore, it can be concluded that this method has better practical value and is easier to be deployed in the current application environment. [ABSTRACT FROM AUTHOR]

In the early stage of evolution, the particle swarm optimization requires large dimensional changes to search for more new regions. Later, only a few dimensions fail to reach the optimal solution and fall into local optimum. Aiming at the above problems, this paper proposed a particle swarm optimization for dynamic spatial variability of superior quality individuals. After selecting the superior quality individuals, it picked two different dimensions, let one of the dimensions fly to the other dimension with Levy flight to obtain the new variation dimension value, and the proceed dimension of variation decreased as the number of iterations increased. The diversity of the population was further enhanced and the exploration and exploitation capabilities were balanced. Compared the proposed algorithm with the state of the art particle swarm optimization variants on the 12 benchmark function, and the experimental results show that the proposed algorithm is more advantageous in convergence speed and precision. [ABSTRACT FROM AUTHOR]

To overcome the local optimum and premature convergence due to loss of population diversity of the particle swarm optimization algorithm, this paper proposed a disruption particle swarm optimization algorithm based on exponential decay weight (EDW -D PSO). Firstly, it semi-uniformly initialized the population to distribute the population in an overall uniform, locally random manner. Secondly, it introduced the dynamic splitting operator to perform splitting operations on particles which satisfying the splitting condition, increasing the diversity of the population and avoiding the particles falling into local optimum. Finally, it used the exponential decreasing inertia weight to balance the global search and local development ability of the particles. The experimental results show that the algorithm has a large search space in the early stage, and the population diversity increases. In the later stage, emphasizing the local development to improve the convergence precision and optimization ability, it can also accelerate particles jumping out of the local extremum and approximate globlal optimum. [ABSTRACT FROM AUTHOR]

In order to improve the performance of particle swarm optimization, this paper proposed a hybrid algorithm based on simplex search and particle swarm optimization. On the one hand, the algorithm adaptively determined the inertia weight, cognition and social parameters for the purpose of avoiding parameters. On the other hand, it used the simplex search to guide the direction of several particles, thus accelerated the convergence of the algorithm. The results of numerical experiments show that the proposed algorithm has better performance than the compared algorithms in terms of function evaluations and accuracy . [ABSTRACT FROM AUTHOR]

For the large demand of particles when particle filter tracking, this paper proposed a particle filter algorithm based on the improved firefly algorithm. Firstly, in order to improve the accuracy of tracking, the improved algorithm introduced the observation value into the brightness formula of the firefly. Secondly, it proposed an adaptive attraction radius parameter to control the attraction range when the fireflies swarm optimized, so that the real-time performance would be better. Finally, it used the iterative optimization of the firefly algorithm to update the particles. Comparison experiments show that, the algorithm has been optimized for tracking accuracy and running time. It shows that the algorithm can guarantee the accuracy and real-time performance of the target tracking even if the number of particles is small. [ABSTRACT FROM AUTHOR]

This paper proposed an improved binary particle swarm optimization (IBPSO) algorithm to solve the controllers' placement problem. IBPSO algorithm determined the current value of particles based on both the global optimal and the individual optimal, thus weakened the influence of particle current value on the next iteration, and accelerated the convergence, which leaded to a better final result. The simulation results show that compared with the BPSO algorithm, the controllers' placement scheme obtained by this algorithm can significantly reduce the number of controllers while realizing the load balancing of the controller. [ABSTRACT FROM AUTHOR]

In order to overcome the shortcomings of the basic bat algorithm, such as easy to fall into local optimum and slow convergence speed, the optimization study was carried out. This paper proposed a new hybrid bat algorithm based on the original features, framework of the basic bat algorithm and specific characteristics of the 0- 1 knapsack problem. The hybrid algorithm introduced the crossover mechanism of the genetic algorithm and the inverse operator to construct a brand-new location transfer method and local search rule. The greedy strategy was added to make the solution feasible and fully utilized, enhancing the local search ability and speeding up the convergence. This hybrid bat algorithm was applied to two sets of 0-1 knapsack cases. The simulation results exceeded adaptive cellular particle swarm optimization, basic bat algorithm and greedy binary bat algorithm. The results verify the feasibility and effectiveness of the hybrid algorithm in solving 0-1 knapsack problem. [ABSTRACT FROM AUTHOR]

The traditional particle swarm optimization algorithm uses constant learning constants for social and self -cognition to limit the population’s global coordination ability. In the late convergence of the algorithm, the diversity of the population was lost and all the individuals converge to one point in search space, which could trigger the precocious convergence. In view of this defect, this paper proposed a chaotic map particle swarm optimization algorithm based on variable learning factor. In the early stage of the algorithm, the emphases should focus on the best location of self-recording. At the later period of the algorithm, it should design a coordinated dynamic learning factor to converge on the best position of population. In order to overcome the premature phenomenon and determine the variance of population diversity below the set value, it used chaotic mapping updated the optimal individual location of the generation and utilized a new way to optimized. The experimental show the new algorithm has better performance in convenience speed and precision. [ABSTRACT FROM AUTHOR]

Geometric constraint solving is a key technique in CAD modeling. This paper researched the problem that solving quality was not high and solving speed was slow, and proposed a method of geometric constraint solving, which combined fish swarm algorithm and chaos algorithm. Firstly, this method denoted geometric constraint relations in CAD model as a group of algebraic equations. Secondly, it used the group of algebraic equations to construct objective function. It transformed a problem of geometric constraint solving into a problem of objective function optimization. Finally, it used chaos algorithm to improve fish swarm algorithm in order to find an optimal solution of objective function. Experimental results show that this proposed method can effectively solve geometric constraint problems. [ABSTRACT FROM AUTHOR]

In order to improve the driving effect of the control system based on MAP and reduce the storage capacity of the control system effectively, this paper proposed a new method for selecting the fixed points in the MAP based on the improved particle swarm optimization algorithm. Taking the MAP of hydraulic mechanical continuously variable transmission ratio control system as an example, it divided 2 variables of the abscissa coordinate in its definition domain, and used the improved particle swarm optimization algorithm to select the number and location of coordinate points in each section after equal division. The selection process was based on the principle of multi-objective optimization, which combined the actual values of 100 randomly generated points with the average error of linear interpolation of MAP and the number of selected scalar points. In order to improve the efficiency of the algorithm, it improved the iterative criteria, the inertia weight and the learning factor of the particle swarm optimization. The results show that the improved particle swarm optimization algorithm has the advantages of fast convergence speed and high optimization accuracy, and the MAP with better control effect can be made with less calibration data . [ABSTRACT FROM AUTHOR]

Aiming at the limitation of traditional optimization algorithm; for blind source separation (BSS), this paper proposed a blind speech separation algorithm based on the improved glowworm swarm optimization (IGSO). The new algorithm constructed a right function to adjust adaptively the flight span,so it could not only accelerate the convergence speed,but also avoided the premature convergence. The mixed speech separation experiments show that the proposed algorithm performs better than BSS based on nature gradient algorithm (NGA), GSO and particle swarm optimization (PSO), and improves both convergence rate and separation ability. [ABSTRACT FROM AUTHOR]

This paper presented a relational database watermarking algorithm based on PSO-BF by adopting swarm intelligence optimization algorithm for reference. The application of chaotic mapping resulted in watermark. The use of one-way hash function secured order of the tuples. It formulated the watermarking of relational databases as a constrained optimization problem, and used PSO-BF algorithm to solve the optimization problem and to handle the constraints. It decoded watermark bits by using threshold-evaluator and majority-voter, then decrypted them to derive the actual watermark sequence by utilizing chaotic mapping. The experimental results show that this algorithm has good robustness to the attacks of tuple deletion, alteration and insertion, and possesses good invisibility. [ABSTRACT FROM AUTHOR]

Two-step method is a commonly used solution to the underdetermined blind source separation of sparse source signals, K-means clustering algorithm is usually utilized to estimate mixing matrix firstly, and then, the shortest path algorithm is used to recover source signals. The source signal's number needed in the K-means clustering algorithm is usually unknown in fact, so have to estimate it. This paper studied clustering validity index :BWP index, combined with particle swarm optimization, proposed a new algorithm to determine the source signal's number. Then it introduced the proposed algorithm into the underdetermined blind source separation. The results of experiment show that the proposed algorithm has good separation precision meanwhile it can shorten the separation time and save some memory this advantage is more obvious when the observation data is huge. [ABSTRACT FROM AUTHOR]