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    Wyświetlanie 1-4 z 4
    Tytuł:
    Investigating multi-objective time, cost, and risk problems using the Grey Wolf Optimization algorithm
    Autorzy:
    Yilmaz, Mehmet
    Dede, Tayfun
    Grzywiński, Maksym
    Powiązania:
    https://bibliotekanauki.pl/articles/31342511.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Częstochowska
    Tematy:
    multi-objective optimization
    grey wolf optimization algorithm
    time-cost-risk
    optymalizacja wielocelowa
    algorytm optymalizacji szarego wilka
    czas-koszt-ryzyko
    Opis:
    Safety plays a crucial role in construction projects. Safety risks encompass potential hazards such as work accidents, injuries, and security. Consequently, it is important to effectively manage these risks with equal emphasis on time and cost considerations during the project planning phase. Within the scope of this research, the grid and archive-based Grey Wolf Optimizer (GWO) algorithm was employed to investigate multi-objective time-cost-risk problems. By employing the GWO, multiple Pareto solutions were provided to the decisionmaker, facilitating improved decision-making. It was determined that the GWO algorithm yields better results in time-cost-risk problems compared to the Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms.
    Źródło:
    Budownictwo o Zoptymalizowanym Potencjale Energetycznym; 2023, 12; 79-86
    2299-8535
    2544-963X
    Pojawia się w:
    Budownictwo o Zoptymalizowanym Potencjale Energetycznym
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Nature Inspired Hybrid Partitional Clustering Method Based on Grey Wolf Optimization and JAYA Algorithm
    Autorzy:
    Shial, Gyanaranjan
    Saho, Sabita
    Panigrahi, Sibarama
    Powiązania:
    https://bibliotekanauki.pl/articles/27312857.pdf
    Data publikacji:
    2023
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    grey wolf optimizer
    JAYA algorithm
    article swarm optimization
    ine-cosinealgorithm
    partitional clustering
    Opis:
    This paper presents a hybrid meta-heuristic algorithm that uses the grey wolfoptimization (GWO) and the JAYA algorithm for data clustering. The ideais to use the explorative capability of the JAYA algorithm in the exploitativephase of GWO to form compact clusters. Here, instead of using only one bestand one worst solution for generating offspring, the three best wolves (alpha,beta and delta) and three worst wolves of the population are used. So, the bestand worst wolves assist in moving towards the most feasible solutions and simul-taneously it helps to avoid from worst solutions; this enhances the chances oftrapping at local optimal solutions. The superiority of the proposed algorithmis compared with five promising algorithms; namely, the sine-cosine (SCA),GWO, JAYA, particle swarm optimization (PSO), and k-means algorithms.The performance of the proposed algorithm is evaluated for 23 benchmarkmathematical problems using the Friedman and Nemenyi hypothesis tests. Ad-ditionally, the superiority and robustness of our proposed algorithm is testedfor 15 data clustering problems by using both Duncan's multiple range test andthe Nemenyi hypothesis test.
    Źródło:
    Computer Science; 2023, 24 (3); 361--405
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Photovoltaic power prediction based on improved grey wolf algorithm optimized back propagation
    Autorzy:
    He, Ping
    Dong, Jie
    Wu, Xiaopeng
    Yun, Lei
    Yang, Hua
    Powiązania:
    https://bibliotekanauki.pl/articles/27309934.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    BP neural network
    photovoltaic power generation
    PSO–GWO model
    PSO–GWO–BP prediction model
    particle swarm optimization
    gray wolf optimization
    back propagation
    standard grey wolf algorithm
    Opis:
    At present, the back-propagation (BP) network algorithm widely used in the short-term output prediction of photovoltaic power stations has the disadvantage of ignoring meteorological factors and weather conditions in the input. The existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. The back-propagation (BP) neural network is easy to fall into local optimization thus reducing the prediction accuracy in photovoltaic power prediction. In order to solve this problem, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the photovoltaic power prediction model of the BP neural network. So, an improved grey wolf optimization algorithm optimized BP neural network for a photovoltaic (PV) power prediction model is proposed. Dynamic weight strategy, tent mapping and particle swarm optimization (PSO) are introduced in the standard grey wolf optimization (GWO) to construct the PSO–GWO model. The relative error of the PSO–GWO–BP model predicted data is less than that of the BP model predicted data. The average relative error of PSO–GWO–BP and GWO–BP models is smaller, the average relative error of PSO–GWO–BP model is the smallest, and the prediction stability of the PSO–GWO–BP model is the best. The model stability and prediction accuracy of PSO–GWO–BP are better than those of GWO–BP and BP.
    Źródło:
    Archives of Electrical Engineering; 2023, 72, 3; 613--628
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Global path planning for multiple AUVs using GWO
    Autorzy:
    Panda, Madhusmita
    Das, Bikramaditya
    Pati, Bibhuti
    Powiązania:
    https://bibliotekanauki.pl/articles/229749.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Autonomous Underwater Vehicle
    AUV
    Genetic Algorithm
    GA
    Global Path Planning
    GPP
    Grey Wolf Optimization
    GWO
    Sliding Mode Control
    SMC
    waypoints
    Opis:
    In global path planning (GPP), an autonomous underwater vehicle (AUV) tracks a predefined path. The main objective of GPP is to generate a collision free sub-optimal path with minimum path cost. The path is defined as a set of segments, passing through selected nodes known as waypoints. For smooth planar motion, the path cost is a function of the path length, the threat cost and the cost of diving. Path length is the total distance travelled from start to end point, threat cost is the penalty of collision with the obstacle and cost of diving is the energy expanse for diving deeper in ocean. This paper addresses the GPP problem for multiple AUVs in formation. Here, Grey Wolf Optimization (GWO) algorithm is used to find the suboptimal path for multiple AUVs in formation. The results obtained are compared to the results of applying Genetic Algorithm (GA) to the same problem. GA concept is simple to understand, easy to implement and supports multi-objective optimization. It is robust to local minima and have wide applications in various fields of science, engineering and commerce. Hence, GA is used for this comparative study. The performance analysis is based on computational time, length of the path generated and the total path cost. The resultant path obtained using GWO is found to be better than GA in terms of path cost and processing time. Thus, GWO is used as the GPP algorithm for three AUVs in formation. The formation follows leader-follower topography. A sliding mode controller (SMC) is developed to minimize the tracking error based on local information while maintaining formation, as mild communication exists. The stability of the sliding surface is verified by Lyapunov stability analysis. With proper path planning, the path cost can be minimized as AUVs can reach their target in less time with less energy expanses. Thus, lower path cost leads to less expensive underwater missions.
    Źródło:
    Archives of Control Sciences; 2020, 30, 1; 77-100
    1230-2384
    Pojawia się w:
    Archives of Control Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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