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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]

Whale optimization algorithm is a new swarm intelligence optimization algorithm inspired by humpback whale hunting behavior, and its performance is far better than the traditional algorithms. In order to track the latest research results and promote the research in the field of swarm intelligence optimization algorithm, this paper firstly introduced the basic principle of whale optimization algorithm, briefly described its implementation steps, and compared and analyzed its pe1formance characteristics. In view of its shortcomings of slow convergence speed and low convergence accuracy, this paper summarized and analyzed the improved methods including other intelligent optimization algorithms, Levy flight strategy, chaos strategy, as well as their advantages and limitations. Then, it described its applications and effect in combinatorial optimization, image segmentation, data prediction, and so on. Finally, this paper summarized and prospected the future development trend. [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]

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]

This paper proposed the mathematical model which based on the open location routing problem with fuzzy demands considering with the sum of location cost,routing cost,lost opportunity and additional distances.For solving the mathematical model,this paper proposed a new swarm intelligence algorithm named hybrid discrete mushroom algorithm.The algorithm used partly mapping crossover and path relinking algorithm to replaced artificial wind of original mushroom reproduction algorithm(MRA),and used the method of selecting different neighborhoods with a certain probability in the local search.It used stochastic simulation program to simulate the designed routes to compute additional distances and lost opportunity resulting from serving failure.The algorithm was successfully applied to the combinatorial optimization problems.Finally,this paper verifies the validity of the model and the performance of hybrid discrete mushroom reproduction algorithm through a series of tested instances and comparisons. [ABSTRACT FROM AUTHOR]

Aiming at the problems of slow convergence speed and easy to fall into local optimum of SSA, this paper proposed an improved salp swarm algorithm combing information feedback sharing and mayfly search mechanism. Firstly, it used piecewise chaos to initialize the population to make the initial salps more evenly cover the feasible space. It adopted the information sharing mechanism to propose an auxiliary leader strategy, in order to improve the leader position update formula, and enhance the global search ability. It used the evolutionary theory and the idea of positive and negative feedback regulation, selecting better leaders through mutation operation and natural selection, so as to improve the search ability precision. Finally, it put forward the mayfly search mechanism, selected the mating formula of the mayfly algorithm to optimize the iterative formula of the follower position and make the algorithm converge faster in the later stage. Experiments on 12 benchmark functions and 17 CEC test functions prove the comprehensive performance of the improved salp population algorithm, and the ablation experiments verifiy the effectiveness of the improved strategy. The experimental results show that the improved algorithm has obvious advantages in convergence speed and search accuracy. [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]

Aiming at the problems of low search accuracy and slow convergence speed of traditional flower pollination algorithm (FPA) in solving complex problems, this paper proposed an improved flower pollination algorithm based on hybrid strategy (HSFPA). It adopted an adaptive transition probability strategy to improve the transition probability and dynamically balance the relationship between global pollination and local pollination. In the stage of global pollination, it proposed a dynamic global search strategy, which could not only accelerate the convergence speed of the algorithm, but also increase the diversity of pollen population and prevented pollen from falling into local optimization. The local search enhancement strategy enabled pollen to fully develop the search space around the current high-quality pollen and improved the convergence accuracy. Pollen cross-border correction strategy further strengthened the exploration ability of the algorithm. Through the simulation test of 10 benchmark functions, the experimental results show that the HSFPA algorithm has better effect in search speed and optimization accuracy. [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]

With the expansion of the fields of mobile robot applications and the complication of its working environment, traditional path planning algorithms become difficult to meet people' s requirements due to their own limitations. In recent years, bioinspired intelligent algorithms ( BIAs) are widely used in mobile robot path planning optimization because of its collective intelligence of the group and the characteristics of biological preference . First, this paper classified the bioinspired intelligent algorithms used in the path planning optimization according to their sources of the biomimetic mechanism. Then, to optimize the latest research results and summarize existing problems and solutions, it described systematically various new bioinspired intelligent algorithms used in the path planning optimization according to different categories, and also provided a detailed comparison and analysis of the algorithms performance in path planning optimization. Finally, it discussed further research direction about bioinspired intelligent algorithms. [ABSTRACT FROM AUTHOR]

Community structure analysis is a critical task in examining a complex network. This paper presented a self-organizing overlapping community structure analysis algorithm (SO2CSA2) based on the swarm intelligence theory. The basic idea behind the algorithm was that it treats an analyzed network as a swarm intelligence system, of which each node was an individual with simple intelligence. Each individual independently decides to which community it joined based on a defined metric named connection score. An individual could join to multiple communities simultaneously. At first, the algorithm found a set of K-cliques from the analyzed network as the initial community structure. Then, each individual in the system acted iteratively to join into or leave from communities, and an optimal community structure of the whole network could develop and eventually emerge from the initial community structure. Finally, to improve the quality of the obtained community structure, a post process adjusted community assignments of a small number of individuals. Experimental evaluation on a number of synthesized networks and real-world networks indicates that the quality of community structures discovered by SO2CSA2 is better than those of two compared algorithms, SLPA and OSLOM, especially on networks with a large number of overlapping nodes or on networks with overlapping nodes of which overlapping degrees are high. [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]