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Di Sini Kalian Bisa nonton Film Drama Dan nonton Movie Terbaru Favorit Kalian, Hampir Semua Koleksi Film Dan Drama Kami Memiliki Subtitles, Jadi Agan Bisa streaming Film Atau download Movie Dengan Nyaman Apalagi Di Dukung Dengan Player Yang Mumpuni No Buffer Nikmati Pengalaman nonton Drama Serial Dan nonton Film Dengan Nyaman Koleksi Drama serial dan movie kami meliputi: Serial Korea, Serial Barat, Serial Mandarin, Serial Silat, Serial Thailand, Serial Filipina, Serial India dan tidak ketinggalan Film Terbaru Box Office Saat Ini Kami Memiliki Film Terbaru Sub Indo 21857 Judul dan Drama Serial Sub Indo 6481 Judul Cinema 21 Nonton drama Bioskop168 Layarkaca 21 Ganool LK21 XXI KshowID LayarKaca21 GrandXXI Drakorindo Samehadaku INDOXXI.It can be concluded that the hierarchy of the ant colony is queen dinergates ergates if they are classified with jobs.Show citation An Improved Grey Wolf Optimization Algorithm with Variable Weights Zheng-Ming Gao 1 and Juan Zhao 2 1 School of Computer Engineering, Jingchu University of Technology, Jingmen, Hubei 448000, China 2 School of Electronics and Information Engineering, Jingchu University of Technology, Jingmen, Hubei 448000, China Show more Academic Editor: Rait Kker Received 11 Dec 2018 Revised 19 Feb 2019 Accepted 13 Mar 2019 Published 02 Jun 2019 Abstract With a hypothesis that the social hierarchy of the grey wolves would be also followed in their searching positions, an improved grey wolf optimization (GWO) algorithm with variable weights (VW-GWO) is proposed.And to reduce the probability of being trapped in local optima, a new governing equation of the controlling parameter is also proposed.
Layarkaca21Semi Movie Kami MeliputiResults show that the proposed VW-GWO algorithm works better than the standard GWO, the ant lion optimization (ALO), the particle swarm optimization (PSO) algorithm, and the bat algorithm (BA). The novel VW-GWO algorithm is also verified in high-dimensional problems. Introduction A lot of problems with huge numbers of variables, massive complexity, or having no analytical solutions were met during the behavior of exploring, exploiting, and conquering nature by human beings. Layarkaca21Semi Free Lunch RuleBut unfortunately, because of the no free lunch rule 1, it is always hard to find a universal efficient way for almost all problems. Therefore, scientists and engineers around the world are still under ways to find more optimization algorithms and more suitable methods. Traditionally, the optimization algorithms are divided into two parts: the deterministic algorithms and the stochastic algorithms 2; the deterministic algorithms are proved to be easily trapped in local optima, while the stochastic algorithms are found to be capable of avoiding local solutions with randomness. Thus, more attention is paid to the stochastic algorithms, and more and more algorithms are proposed. Among the research on the stochastic algorithms, presentations, improvements, and applications of the nature-inspired computing (NIC) algorithms come into being a hot spot. The NIC algorithms are proposed with inspiration of the nature, and they have been proved to be efficient to solve the problems human meet 3, 4. ![]() They can solve problems with parallel computing and global searching. The metaheuristic algorithms divide the swarms in global and local searching with some methods. They cannot guarantee the global optimal solutions; thus, most of the metaheuristic algorithms introduce randomness to avoid local optima. The individuals in swarms are controlled to separate, align, and cohere 8 with randomness; their current velocities are composed of the former velocities, random multipliers of the frequency 9, or Euclidean distances of specific individuals positions 10 14. Some improvements are made with inertia weights modification 15 17, hybridization with invasive weed optimization 18, chaos 19, and binary 20 vectors et al. Most of these improvements result in a little better performance of the specific algorithms, but the overall structures remain unchanged. Almost all of the metaheuristic algorithms and their improvements so far are inspired directly from the behaviors of the organisms such as searching, hunting 11, 21, pollinating 13, and flashing 14. In the old metaheuristic algorithms, such as the genetic algorithm (GA) 22, simulated annealing (SA) 23, and the ant colony optimization (ACO) algorithm 24, the individuals are treated in the same way, and the final results are the best fitness values. Metaheuristic algorithms perform their behavior under the same governing equations. To achieve a better performance and decrease the possibility of being trapped in local optima, random walks or levy flights are introduced to the individuals when specific conditions are might 25, 26. These mostly mean that the swarms would perform their behavior in more uncontrolling ways. Furthermore, as organisms living in swarms in nature, most of them have social hierarchies as long as they are slightly intelligent. For example, in an ant colony, the queen is the commander despite its reproduction role; the dinergates are soldiers to garden the colony, while the ergates are careered with building, gathering, and breeding.
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