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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.)

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.)

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.)

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.)

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.)

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.)

From the perspective of the surgical scheduling problem in emergency, this paper considers the fatigue effect and the truncated learning effect caused by medical staffs' long-time work, regards the surgical scheduling problem as the hybrid flow-shop scheduling problem and proposes a three-stage surgical scheduling model which includes preoperative, intraoperative and postoperative. The improved grey wolf optimization is used to solve the emergency surgical scheduling model, and the effectiveness of the model and the algorithm are tested through simulation experiments. The results show that the improved grey wolf optimization algorithm can solve the emergency surgical scheduling model more effectively and get better scheduling results. [ABSTRACT FROM AUTHOR]

Knapsack problem with a single continuous variable (KPC) is a natural generalization of the standard 0-1 knapsack problem. In KPC, the capacity of the knapsack is not fixed, so it becomes more difficult to solve. In order to overcome the shortcoming that the performance of existing differential evolution (DE) algorithm is not good enough on highdimensional KPC instances, this paper proposes Lamarckian evolution-based DE (LEDE) algorithm to solve KPC. The improvement generated by the greedy repair and optimization operator is inherited to the offspring, which can speed up the convergence speed of DE algorithm and improve the precision of DE algorithm on high-dimensional KPC instances. At the same time, a profit-based greedy optimization strategy is introduced in the greedy repair and optimization operator to optimize the feasible solution generated by greedy repair strategy based on profit weight ratio, so as to help the algorithm jump out of local optimum. The LEDE algorithm is experimentally analyzed on 40 KPC instances. The experimental results show that the Lamarckian evolution and the profit-based greedy optimization strategy can improve the exploitation ability of LEDE algorithm, and LEDE algorithm is better than other intelligent optimization algorithms in terms of obtaining the best solution and the mean solution. [ABSTRACT FROM AUTHOR]

Aiming at the problem that Harris hawks optimization algorithm converges slowly and is prone to local optimization, this paper proposes an improved Harris hawks optimization algorithm (IHHO) which combines mutually beneficial symbiosis and lens imaging learning. Firstly, the algorithm uses Tent chaotic map to initialize the population to increase the diversity of the population and improve the optimization performance of the algorithm. Secondly, in the exploration stage, the algorithm integrates the idea of mutually beneficial symbiosis and nonlinear inertia factor to enhance the exchange of population information and accelerate the convergence speed. Then, the algorithm uses lens imaging reverse learning strategy to perturb and mutate the Harris hawks position with a certain probability to improve the ability of the algorithm to jump out of the local optimum. Finally, the simulation results of 16 benchmark test functions show that IHHO has faster convergence speed, higher precision and stronger robustness compared with the other five algorithms. At the same time, IHHO is applied to the problem of image segmentation, and the simulation results verify the feasibility of the algorithm in practical engineering applications. [ABSTRACT FROM AUTHOR]

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.)

Aiming at the shortcomings of particle swarm optimization (PSO) algorithm, which is easy to fall into local optimum and low precision during iteration, this paper proposes a self-conclusion and self-adaptive variation particle swarm optimization (SCVPSO). Firstly, the position of each particle is dynamically updated by nonlinear turning up and then decreasing inertia weight to avoid premature. Secondly, the local particles are searched backward to improve the efficiency of population optimization. Finally, a new parameter scr (self-conclusion rate) is introduced to summarize the recent solution situation of each particle, and the probability directed variation is used to guide the particles to the global optimum to increase the diversity of particles. With the help of 15 test functions, compared with other variant particle swarm optimization algorithm, the results show that the improved algorithm is significantly better than other algorithms in solving performance, which verifies the effectiveness of the strategy. [ABSTRACT FROM AUTHOR]

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.)

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.)