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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]

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]

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

In order to solve the problem that 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]

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 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]

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]

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]

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]

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]

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]

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]