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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 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]

The U-turn task is one of the contents of autonomous driving research, and most of the solutions under the standard roads in cities cannot be implemented on non-standard roads. Aiming at solving this problem, this paper establishes a vehicle U-turn dynamical model and designs a multi-scale convolutional neural network to extract feature maps as the input of the agent. In addition, for the sparse reward problem in the U-turn task, this paper proposes a hierarchical proximal policy optimization algorithm that combines hierarchical reinforcement learning and proximal policy optimization algorithm. In experiments with simple and complex scenarios, this algorithm learns policies faster and has a higher success rate of U-turn compared to other algorithms. [ABSTRACT FROM AUTHOR]

In order to solve the problem of premature convergence and falling into local optimum in biogeography-based optimization algorithm, this paper proposed an improved biogeography-based optimization algorithm introducing species evolution (SEBBO). The algorithm divided all habitats into three regions according to the number of species, established a co-evolution relationship, and reasonably adopted interval invasion and regional cooperation/competition strategies to meet diversity while avoided premature convergence. It defined two mutation strategies of species change and species development, and designed the dual-strategy cooperative mutation operator to solve the damage of the mutation operator to the better solution. Then, compared with standard BBO and other related improved algorithms on 8 benchmark functions from CEC2017, SEBBO algorithm is superior to BBO and other BBO variants in terms of algorithm performance and stability, and local optimal values is not easily limit the algorithm. Finally, this paper applied the SEBBO algorithm to flexible Job-Shop scheduling with maximum completion time as the goal to test its practical application value. Experiments show that the algorithm has a certain effectiveness in solving FJSP. [ABSTRACT FROM AUTHOR]

To solve the problems of low convergence accuracy and easy to fall into local optimum in the optimization process of the original artificial bee colony algorithm, this paper proposed an enhanced artificial bee colony algorithm with strong and weak ties theory (SWT-ABC). Firstly, it defined the strong and weak ties model in a matrix multi-groups structure and stipulated that the strong ties individuals randomly guided the search process from three directions to improve the convergence speed and accuracy. In order to enhance the ability to jump out of the local optimum, SWT-ABC adopted the interaction of weak ties individuals among subgroups to enhance the population diversity. Finally the algorithm added the reverse learning mechanism of scout bees and determined appropriate upper limit of honey source to improve the utilization efficiency of the evaluation times. Through the numerical experiment of benchmark functions and comparisons with 12 improved intelligent optimization algorithms, it shows that the SWT-ABC has high convergence accuracy and good robustness, and also gets good performance in solving high-dimensional optimization problems. [ABSTRACT FROM AUTHOR]

Nowadays, how to reduce the execution cost of workflow under the premise of meeting the deadline constraint is one of the main problems of workflow scheduling in cloud. A three-step list scheduling algorithm can solve this problem effectively. However, the algorithm can only form static sub-deadline in deadline allocation stage. To provide convenience for users to deploy workflow tasks, cloud service providers provide users with three types of instances. Among them, spot instance has great price advantage and can greatly reduce the execution cost of workflow. To solve the above problems, this paper proposed a cost optimization algorithm based on dynamic allocation of deadline for workflow scheduling (S-DTDA). This algorithm used particle swarm optimization to dynamically allocate the deadline, which made up for the defect of the three-step list scheduling algorithm. In the virtual machine selection stage, the algorithm added the spot instance to the candidate resources, which greatly reduced the execution cost. Experimental results show that compared with other classical algorithms, this algorithm has obvious advantages in experimental success rate and execution cost. In conclusion, S-DTDA algorithm can effectively solve the cost optimization problem of deadline constraint in workflow scheduling. [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 three-dimensional path planning problem is a combinatorial optimization problem to find the optimal path be- tween the starting point and the destination in the interference environment. Aiming at the shortcomings of traditional swarm in- telligence algorithm in solving this problem,such as low convergence accuracy and easy to fall into local optimum, this paper proposed an adaptive moth-flame optimization algorithm to solve the problem. By introducing the dynamic adjustment strategy of the flight direction and the position intersection strategy,the improved algorithm continuously generated new individuals while dynamically adjusting the flight direction of the moth, which effectively avoided the algorithm from falling into local optimum ; by adaptively adjusting the number of flames, so as to the algorithm enhanced population diversity in the global exploration stage a- void premature convergence. It used the adaptive moth-flame optimization algorithm and other swarm intelligence algorithms to solve the three-dimensional path planning problem. The experimental results show that the improved adaptive moth-flame opti- mization algorithm has the smallest cost value and the fastest convergence speed among all algorithms. The algorithm has better solution ability in 3D path planning problem. [ABSTRACT FROM AUTHOR]

In order to solve the problems of lack of state labels, lack of data samples and unbalanced distribution in equipment life prediction, this paper proposed an improved K-means algorithm based on PSO and a data optimization scheme based on traditional SMOTE. In the process of optimizing K-means algorithm, it combined the characteristics of particle swarm optimization algorithm, and improved the optimization efficiency of particle swarm optimization algorithm by giving the particle generation range of particle swarm optimization algorithm, so as to quickly judge the working state of the equipment. Then, by comparing the mean distance of samples in the same cluster with the distance from the sample to the center, it established an improved SMOTE algorithm, and avoided the calculation error caused by the imbalance of samples by adding the number of minority samples. Finally, this paper used AdaBoost integrated optimization KNN algorithm to improve the classification effect, and by fitting the equipment life curve, it could better predict the equipment health level and future life. The example shows that the proposed model can effectively predict the health status of equipment under small sample unbalanced data. [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]

Pose graph optimization（PGO） is a high dimensional non-convex optimization algorithm widely used in the field of computer vision. It is hard to solve directly and mainly relies on iterative techniques. It requires high quality of initial value which is difficult to be guaranteed in practice. This paper studied PGO problem and proposed a simple closed solution algorithm for pose graph based on eigen decomposition. This algorithm first developed the semidefinite relaxation of maximum-likelihood estimation（MLE） for PGO problems. Then it transformed MLE into eigen decomposition problem, and designed an improved model reduction method to solve the problem by using the sparsity of data, which further improves the computational speed of the algorithm. The algorithm has the advantages of scalability, low computational cost and high precision. Finally, experimental evaluation on simulated and real-world pose-graph datasets shows that the proposed algorithm can optimize the pose graph quickly without compromising accuracy. [ABSTRACT FROM AUTHOR]

In order to solve the problems of BBO such as insufficient search scope in the early stage and easy to fall into local optimization in the later stage, this paper proposed an improved El-BBO which based on El. Firstly, the algorithm searched for new habitat around the original habitat, it enhanced the diversity of the initialization population. Secondly, the algorithm made improved habitat expansion, it improved the convergence efficiency of the algorithm. Finally, the algorithm used gradient de scent to make quadratic convergence in the field of optimal solution, which improved the convergence accuracy of the algorithm. This paper carried out 50 Monte Carlo experiments on 12 optimized test functions commonly used in CEC2014, the experimental results show that the overall performance of El-BBO is better than the other three intelligent optimization algorithms in terms of optimal fitness value, average fitness value and standard deviation. It shows that El-BBO can improve the ability to find the optimal solution and enhance the search stability. [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]

Aiming at solving the problem of convergence defect in the traditional gray wolf optimization algorithm ( GWO), this paper proposed an adaptive gray wolf optimization algorithm ( AGWO) with hybridization strategy. The algorithm firstly introduced a non-linear convergence factor to balance the global search and local development of the algorithm. Secondly, it introduced a genetic hybrid strategy, which crossed the gray wolf population in pairs with a certain probability to produce new individuals, thereby effectively enhancing the diversity of the gray wolf population. At the same time, in order to avoid the algorithm falling into the local optimal solution in the later stage, inspired by the manta ray foraging strategy, the manta ray foraging strategy was introduced and a dynamic adaptive adjustment factor was added to adjust the diversity of the population, which effectively improved the convergence accuracy of the algorithm and global optimization performance. Numerical experiments of selecting 11 benchmark test functions in CEC2014 demonstrate that the proposed algorithm achieves better performance of higher convergence speed and optimization. [ABSTRACT FROM AUTHOR]

The standard whale optimization algorithm( WOA ) and some derivative algorithms are studied and experimented to solve the problem of poor results of some examples. It is proved that there is a zero searching preference trap in the " encircling" process of WOA. In addition, the unbalanced search characteristics of chaos optimization algorithm( COA) make it difficult to reconcile the chaos initial population and swarm intelligence optimization process. In order to improve the above defects, this paper selected two chaotic systems and bubble net hunting strategy, and designed a set of fusion optimization algorithm. The algorithm adopted the baseline adaptive oscillation group partition strategy based on fitness to guide the group behavior pattern, gave full play to the role of chaotic system, and balanced the exploration and convergence performance. It used the new algorithm to solve the general/improved examples and engineering application cases. It 's obviously that the performance of the algorithm is better than the contrast group algorithm,and there is no searching preference. [ABSTRACT FROM AUTHOR]

Swarm intelligence algorithm has become a hotspot in the field of evolutionary algorithm because of its strong dynamic optimization ability and simple implementation approach. The choice of control parameters has a great influence on the optimization performance of the algorithm. Firstly, this paper studied the particle swarm parameters from the point of mathematical derivation, then proposed an improved algorithm with inverse thinking curve increment strategy, which fitted the evolution formula of the particle itself. Finally, it verified that the algorithm has the following two outstanding advantages. It can effectively avoid the problem of precocity. In dealing with the problem of dimensional disaster, the optimization performance is stronger and has a good balance between global and local optimization performance. The algorithm is simple and can effectively solve the problem of low robustness and tedious manual parameter adjustment. [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 users deep interests and preferences cannot excavate in the recommendation. algorithm, which leads to low extraction accuracy and high time complexity of similar users clustering accuracy, this paper proposed a clustering optimization recommendation algorithm (MT-QRPD) based on the evaluation quantization model to optimize differential evolution. Firstly, it used the feature timing of BiGRU network and the strong local feature effectiveness of CNN network to extract the comment depth features, and used the multi-dimensional semantic feature screening of multi-head attention mechanism to mine the comment depth semantic features. Then, through the nonlinear transformation of the multi layer perceptron carried out the multi-feature fusion to achieve accurate quantification. Finally, the algorithm optimized the clustering of similar users with PCA, and completed the project recommendation. The result of experiments shows that the pro posed algorithm can improve the accuracy, time complexity and recommendation performance. [ABSTRACT FROM AUTHOR]

In this paper, a super-resolution(SR) method through an internal and external dictionary-based regularization was proposed for improving the resolution of single low-resolution (LR) image. Firstly, the input was divided in to several similar groups, and then the principal components analysis (PCA) dictionary learning method was used to construct the internal dictionary of each corresponding group. Secondly, the high-frequency details of external high-resolution(HR) images was divided in to groups with similar structures, and the PCA method was adopted to construct the external dictionary corresponding to each group. Next, the nonlocal regression model was used to design two complementary regularities to solve the problem of SR uncertainty. Finally, the SR reconstruction was achieved through an iterative optimization algorithm with gradient descent. The experimental results showed that the proposed method could improve the peak signal to noise ratio(PSNR) by 0.2d Bon average and the structural similarity(SSIM) by 0.01 on average, showing better visual quality than the other compared methods. [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, such as novel COA has slow convergence and insufficient global search ability when solving complex optimization problems, this paper proposed an algorithm called GCCOA. Firstly, during the growth of the coyotes in packs, it embedded a global-best guidance coyote to make the exploitation more powerful and to improve the convergence quality of COA. Then, it adopted a cultural tendency with coyo te interaction, which was impacted by the interaction of the coyotes in the pack, to improve the global search ability of COA. Finally, it applied GCCOA to complex function optimization and image enhancement. A lot of experimental results on 29 complex functions from CEC2017 set show that GCCOA obtains 22 times of the first place and has better optimization ability than COA, HFSPO, CSPSO and 13-GWO. In additional, the experimental results on medical image enhancement show that GCCOA can solve the optimization problem better than comparison algorithms such as COA and so on. So GCCOA is an optimization algorithm. [ABSTRACT FROM AUTHOR]

In order to solve the increasingly complex problem of multi-DAV collaborative task allocation, this paper used an improved hierarchical particle swarm optimization algorithm ( HGIWPSO ) to obtain the optimal allocation scheme. Firstly, according to particle fitness, it divided the population dynamics into three different classes, and selected the appropriate learning model according to the characteristics of different classes of particles, and introduced the independent weight idea to adjust the weight of the inertia weight that imp roved the performance of the algorithm. Then, it established the model of collaborative multi task assignment problem, and adopted the redundant load auction scheme to reduce illegal solutions. It established the mapping relationship between the particle and the actual allocation scheme through real number coding to solve the actual allocation problem. Finally, the experimental results show that the algorithm can effectively solve the problem of multi-UAV coordinated task assignment under complex constraints and obtain the optimal distribution sequence. It has certain theoretical and practical significance. [ABSTRACT FROM AUTHOR]

In order to do further research on enigmatical knapsack problems, this paper proposed an economic model based on dynamic expectation efficiency, and established a new optimization algorithm of 0-1 knapsack problem after analysis and research with the traditional theory of solving knapsack problem. And this paper gave the individual experiment and comparison experiment with artificial glowworm swam algorithm. The results of experiment show that the algorithm is better than greedy algorithm, backtracking algorithm, dynamic programming algorithm and bound algorithm in the same 0-1 knapsack problem. In comparison with artificial glowworm swam algorithm, this algorithm improves convergence speed largely and saves the storage space, and the convergence speed is ten times as the artificial glowworm swam algorithm. Finally, this paper gave 20 0-1 knapsack problems, proved the feasibility of the algorithm, and determined an adaptive scope of the algorithm. [ABSTRACT FROM AUTHOR]

The optimization of network topology structure is an important research direction on the research of interconnection network. Locally twisted cube (LTQn) is the optimized variant of hypercube (Qn). However, if LTQn is upgraded, it will multiply the number of nodes of the network, which goes against the application and development of LTQn. In order to solve this problem, this paper proposed a kind of new network topology structure: locally twisted cube-connected ring interconnect network (LRN). It gave the definition of LRN and its topology structure, and studied the problems on the diameter, connectivity, Hamilton-connectivity, pancyclicity and routing of LRN. At last it proves the LRN is a kind of hierarchical ring interconnection networks (HRN), and it is easy to upgrade and has many excellent properties of LTQn. [ABSTRACT FROM AUTHOR]

In order to overcome the problem of low solution precision of traditional nonlinear constraint algorithms, this paper-proposed a new optimization algorithm based on particle filter. It used this algorithm to solve nonlinear constraint problem, and combined the model and mechanism of particle filters. It established the optimization algorithm based on particle filter algorithm, and gave the procedures of which as well. Then it converted the nonlinear constraint optimization problems to function optimization problems, and established mathematical models of particle filter optimization algorithm for the nonlinear constraint optimization problems. Simulation results verify the validity of the new algorithm, and show that the optimization method based on particle filter has higher efficiency and rate than the traditional optimization algorithm when solving nonlinear optimization problems. Meanwhile, the proposed algorithm has its feasibility to future research on nonlinear constraint optimization problems. [ABSTRACT FROM AUTHOR]

Resource leveling problem is a NP-Hard problem, and this paper proposed a new chonal cuckoo search algorithm to efficiently to solve it. First, the algorithm executed population expansion based on individual's fitness. Then, it employed the Levy mutation operator to realize the clonal population's update. At last, it embedded a non-uniform mutation operator to make a balance between exploration and exlpoiton. The case study illustrates that the clonal cuckoo search algorithm outperforms PSO algorithm, DE algorithm and basic cuckoo search algorithm when consider solution quality and covergence speed as the main mertric of performance. [ABSTRACT FROM AUTHOR]

This paper proposed two kinds of measurements based on close degree between the cloud models had the same nature concepts. Algorithms were simple, especially still maintained the accuracy in few cloud drops. The experimental results show that the proposed algorithm has obvious optimization accuracy and consumption, meanwhile appeares good feasibility and validity in classification problems based on cloud model. [ABSTRACT FROM AUTHOR]

For the flexible Job-Shop scheduling problem (FJSP), this paper proposed the improved biogeography-based optimization algorithm (BBO), modified the migration operator and mutation operator. In the initial stage of the algorithm, it adopted hybrid initialization approach to improve the quality of the initialization population. Then it used different methods of migration operator and mutation operator to improve the ability of global search and to accelerate the convergence speed. By programming and simulation to solve the benchmark problem of FJSP, it compared results with the other results in the literature. Computational results show that the proposed BBO algorithm is an effective and efficient approach and can also be used for other shop scheduling problems. [ABSTRACT FROM AUTHOR]

In order to improve the static stiffness of vehicle door, this paper used HDMR (high dimensional model representation) based multi-parameters decoupling theory combined with Morph technology to optimize main cross section shapes of a vehicle door in the conceptual design stage. It compared with other popular metamodel techniques, this method could identify the coupling relationships among shape variables by a few sample points, and then decomposed the high-dimensional problem into a series of low-dimensional sub-problems, so it could improve the efficiency remarkably without loss of accuracy. It adopted Kriging model to construct the terms of Cut-HDMR and then abtained the displacement approximate models of investigated points under five stiffness conditions. Based on the Kriging-HDMR models, then it used NSGA-II (non-dominated sorting based genetic algorithm) to optimize the approximate model. The results show that it lightens the weight of the vehicle door and improves both the vertical stiffness and the torsional stiffness. [ABSTRACT FROM AUTHOR]

In order to solve the poor robustness of software watermarking and the low execution efficiency of watermarking sharing algorithm, this paper proposed a sharing software watermarking scheme based on chaotic optimization. The scheme took advantage of chaos system, matrix partition and chaotic scrambling the watermarking information to form sharing watermarking. When watermarking was embedded, using DPPCT topology graph encode the sharing watermarking, then putting the hash value of the watermarking information into the info field of each DPPCT. After the watermarking embedded, using chaotic encryption to protect all code and prevent reverse engineering and other methods to attack the software watermarking. Theoretical analysis and experimental results show that the scheme can effectively resist various semantics preserving transformation attacks, decrease the program load and improve robustness and execution efficiency of the watermarking. [ABSTRACT FROM AUTHOR]

This paper proposed cellular bat algorithm to solve 0-1 programming problem, which was based on the principles of cellular automata and bat algorithm. It used cellular and its neighbor to enhance the diversity of the searching process to improve the ability of global optimization, and transformed the constrained problem into the unconstrained problem using penalty function and then described the detailed procedures of the algorithm and programmed with MATLAB software. By some 0-1 programming problem test and comparisons with other algorithms, the results show that cellular bat algorithm has fast convergence speed and better global optimization ability. It can be used as a practical approach to solve 0-1 programming problem. [ABSTRACT FROM AUTHOR]