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Aiming at overcoming drawbacks of imbalance between the global and local search ability of a single heuristic algorithm, this paper proposed a slime mould and flower pollination hybrid algorithm based on dynamic dual population, named HASMFP. Firstly, HASMFP adopted a grouping mechanism that took the distance between individual inside population and the current optimal individual into consideration to dynamically divide whole population into slime mold subpopulation and pollen subpopulation to balance the exploration and development capabilities of the algorithm more effectively. Secondly, HASMFP used a ranking mechanism based on similarity and fitness to improve diversity of slime mold population, and further increased the probability to jump out of local optimal. Finally, HASMFP also adopted a dynamic weights and constant shrinkage coefficients with an elite guidance terms to further enhance the local and global search ability of standard flower pollination algorithm at the same time. It used 12 test functions from CEC2017 test suit as the testbed to evaluate the performance of HASMFP with other 5 algorithms: ISMA, DTSMA, HLFPA, SCFPA, and tMFPA. It conducted ablation experiments to evaluate the effectiveness of all improvement strategies applied in HASMFP. Experimental result shows that HASMFP can rank first under the combination of all improvement strategies. The result of Friedman test based on experimental data illustrates that HASMFP can achieve the supreme performance among all evaluated algorithms. [ABSTRACT FROM AUTHOR]

Aiming at overcoming drawbacks of lower accuracy and being trapped easily into local optimums of the sparrow search algorithm(SSA), this paper proposed an enhanced sparrow search algorithm by integrating niche mechanism(NSSA). Firstly, NSSA adopted an adaptive niche mechanism based on producers' similarity to automatically partition the whole sparrow population into several producer-centered speciation, and to move scroungers inside the speciation towards to the producer inside the same speciation, in order to maintaining the diversity of the whole population. Secondly, NSSA applied a fuzzy logic based differential mutation operation to further mutate sparrow producers to raise the probability to jump out of local optimums. It used 12 functions selected from the CEC2017 benchmark function as the testbed to evaluate NSSA with CSSOA, ISSA, MSSSA, SHSSA, and MSWOA. Experimental results show that NSSA even can raise convergence accuracy up by 10% compared with the second ranked evaluated algorithm on most of multimodal objective test functions. This paper also conducted ablation experiments for each improved strategy proposed in NSSA. Experimental results show NSSA can achieve supreme performance by applying all 3 strategies simultaneously. At last, NSSA is applied to solve the pressure vessel design problem. Experimental results illustrate that NSSA has the ability to find the best solutions compared to other evaluated algorithms. [ABSTRACT FROM AUTHOR]

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Aiming at overcoming drawbacks of sparrow search algorithm (SSA), such as poor population diversity and being easy to fall into local optimum, this paper proposed a multi-strategy collaborative improved sparrow search algorithm (ISSA). At first, ISSA adopted a boundary learning strategy incorporating transfer probability to update the location of the discoverer, expanding the search scope of the discoverer and enriching its population diversity. Secondly, ISSA introduced a hybrid particle swarm optimization mechanism in the sparrow follower update process to expand the selection range of target follower individuals. Finally, during the optimization process of the algorithm, ISSA used a fuzzy inference system to dynamically monitor the probability of the population falling into the local optimum, and used differential mutation operations to improve the ability of the population to jump out of the local optimum. This paper selected 12 test functions from CEC2017 testbed as benchmark to evaluate the performance of ISSA with standard SSA, and other 4 improved sparrow algorithms (ESSA, CSSOA, SSASC, and MSSA). Friedman test based on experimental data shows that ISSA can achieve better performance. [ABSTRACT FROM AUTHOR]

Aiming at the problems that SSA is prone to fall into local optimal and relatively low accuracy during search iteration. This paper proposed an enhanced sparrow search algorithm with multiple strategies (ESSA). At first, ESSA applied Lévy flight and cloud based adaptive weights to refine the original discoverers’ position update equation, which expanded the search range and enriched population diversity of the algorithm. Secondly, ESSA adopted a fuzzy control based adaptive lens imaging strategy to get the reversed position of the current optimal position to enhance the algorithm’s ability to jump out of local optimal. This paper selected 12 test functions from CEC2017 testbed as benchmark to evaluate the performance of ESSA with standard SSA, and other 4 improved sparrow algorithms: ISSA, MSSSA, HSSA, and SHSSA. Experiment result shows that ESSA can achieve the supreme results among evaluated algorithms. This paper also applied ESSA to the 3D UAV path planning problem. The simulation result illustrates that ESSA can also find the supreme 3D paths for UAV. [ABSTRACT FROM AUTHOR]

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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

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

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

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Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cloud segmentation is a key task in applications of Landsat8 remote sensing images. At present, most existing deep learning based approaches usually adopt the U-type coder-encoder network structure. Although this structure can make use of spatial location information from the coding end effectively, the whole network has too many parameters and cause high computation cost. Meanwhile, the coding end only adopts simple convolution and subsampling operation, which makes it impossible to obtain high-quality contextual semantic feature information effectively. In view of the above situation, this paper proposed a new lightweight U-Net model for cloud segmentation. The whole model adopted a new U-shaped codec structure that jumped and connected the information between the shallow layer and the middle layer at the coding end, and also embedded novel optimization blocks consist of grouping convolution and attention mechanism in coding end. It also built context-semantic fusion connection to link corresponding layers in coding and decoding end. Experimental results on the common benchmark dataset 38-Cloud illustrates, compared with other mainstream cloud segmentation networks, the proposed new U-Net can achieve better segmentation precision with less model parameters. [ABSTRACT FROM AUTHOR]

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at solving the premature convergence of YYPO algorithm, this paper introduced SA strategy into the switch phase between P, and P2 in YYPO, and proposed a novel hybrid single-object optimization algorithms YYPO-SA ( including YYPO-SAl and YYPO-SA2). YYPO-SA inherited the lightweight characteristics of YYPO, and combined the YYPO' s excellent global search ability with the powerful local search performance of SA. It used the proposed 28 test functions in the single objective practical parameter algorithm competition in 2013 Evolutionary Computing Conference as the benchmark. It empirically studied the performance of YYPO-SA2, YYPO, A-YYPO, IYYPO, and 3 state of the art algorithms-GWO, WOA, and SCA based on experimental results. The experimental res ults show that YYPO-SA can perform more stable and achieve more accurate solutions. At the end, this paper also applied YYPO-SA to solve the spring design task. The experimental results show that YYPO-SA can also solve the constrained optimization task well. [ABSTRACT FROM AUTHOR]

Copyright of China Brewing is the property of China Brewing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Yin-Yang-pair optimization algorithm is a novel lightweight optimization algorithm, which uses the iterative exchange of two points( the global exploration point P2 and the local exploitation point P1 ) to achieve the search of optimal solutions. The user defined parameters of the YYPO directly affect the balance of global exploration and local exploitation of the algorithm and impact the performance of the algorithm. In order to improve the optimization performance of the algorithm, this paper analyred effects of the user-defined parameters( expansion/ contraction factor a) to the performance of the original algorithm, then proposed the IYYPO algorithm. IYYPO adopted linear and non-linear strategy to gradually reduce user-defined parameters. This paper also used 28 test functions, which was the test benchmark of the competition of single objective real parameter algorithm in 2013 Evolutionary Computing Conference,as the testbench to evaluate the performance of three new algorithms. Compared with the original algorithm,experiment results illustrate that the proposed algorithm can archive higher accuracy and faster convergence speed. At last, this paper tested the improved algorithms in a practical optimization problem to show the performance of the improved algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Qing History Journal is the property of Renmin University of China, Institute of Qing History and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)