A fair, efficient, and reasonable strategy for benefit transfer to multi-stakeholders is critical for preserving cooperation stability and development sustainability for cascade reservoirs under current comprehensive ...
详细信息
ISBN:
(数字)9789811910531
ISBN:
(纸本)9789811910531;9789811910524
A fair, efficient, and reasonable strategy for benefit transfer to multi-stakeholders is critical for preserving cooperation stability and development sustainability for cascade reservoirs under current comprehensive watershed development. This research proposes a novel approach to cascade reservoir planning that considers the need for multi-objective water resource development and benefit distribution to multiple stakeholders. The cascade reservoirs planning is modeled as the optimal dispatching model function and optimized by applying an enhanced harmony search algorithm (EHSA). Compared results with the previous methods show that the proposed method with EHSA produces total power generation higher and the calculation time faster convergence speed, respectively, than other competitive methods. The proposed method provides a feasible way to solve the optimal dispatching model of cascade reservoir planning in power generation.
In this article, an improved harmony search algorithm (IHSA) that utilizes opposition-based learning is presented for solving the maximal covering location problem (MCLP). The MCLP is a well-known facility location pr...
详细信息
In this article, an improved harmony search algorithm (IHSA) that utilizes opposition-based learning is presented for solving the maximal covering location problem (MCLP). The MCLP is a well-known facility location problem where a fixed number of facilities are opened at a given potential set of facility locations such that the sum of the demands of customers covered by the open facilities is maximized. Here, the performance of the harmony search algorithm (HSA) is improved by incorporating opposition-based learning that utilizes opposite, quasi-opposite and quasi-reflected numbers. Moreover, a local search heuristic is used to improve the performance of the HSA further. The proposed IHSA is employed to solve 83 real-world MCLP instances. The performance of the IHSA is compared with a Lagrangean/surrogate relaxation-based heuristic, a customized genetic algorithm with local refinement, and an improved chemical reaction optimization-based algorithm. The proposed IHSA is found to perform well in solving the MCLP instances.
To improve the performance of the harmony search algorithm and enable the processing of increasingly complicated optimization problems, a global harmony search algorithm based on tent chaos map and elite reverse learn...
详细信息
ISBN:
(数字)9781665470452
ISBN:
(纸本)9781665470469;9781665470452
To improve the performance of the harmony search algorithm and enable the processing of increasingly complicated optimization problems, a global harmony search algorithm based on tent chaos map and elite reverse learning (HS-TE) has been proposed. The algorithm uses the tent chaos map to initialize the population and adopts the elite reverse learning strategy to optimize the iterative process. The method reduces the algorithm's dependence on the initial solution, improves the search optimization ability, enhances the diversity of the population, and establishes adaptive parameters to control the development and exploration of the iterative process, which is beneficial to improving the algorithm's search ability. Create test experiments: Various HS algorithms perform classic benchmark function tests. The experimental test data shows that the algorithm is better than the current five improved harmony search algorithms and has better convergence and accuracy. The algorithm is used to improve the penalty parameters and kernel function parameters of SVR, and then use the optimized SVR to perform regression prediction on the daily opening number of the Shanghai Stock Exchange. According to the experimental results, the upgraded SVR provides better prediction performance. It works both in theory and in real life and can be used to predict the Shanghai Securities Composite Index.
Aiming at the problem that the diversity of the current double population algorithm with dynamic population size reduction cannot be guaranteed in real time in iteration and is easy to fall into local optimum, this st...
详细信息
Aiming at the problem that the diversity of the current double population algorithm with dynamic population size reduction cannot be guaranteed in real time in iteration and is easy to fall into local optimum, this study presents a dual population collaborative harmony search algorithm with adaptive population size (DPCHS). Firstly, we propose a dual population algorithm framework for improving the algorithm global search capability. Within this framework, the guidance selection strategy and information interaction mechanism are integrated to strengthen the competition and cooperation among populations, and achieving a good balance between exploration and exploitation. A population state assessment method is designed to monitor population changes in real-time for enhancing population real-time self-regulation. Additionally, population size adjustment approach is designed to adopted to effectively streamline population resources and improve population quality. Comprehensive experiment results demonstrate that DPCHS effectively addresses system reliability-redundancy allocation problems with superior performance and robust convergence compared with other HS variants and algorithms from different categories. Graphical Abstract
The patient admission scheduling (PAS) problem is an optimization problem in which we assign patients automatically to beds for a specific period of time while preserving their medical requirements and their preferenc...
详细信息
The patient admission scheduling (PAS) problem is an optimization problem in which we assign patients automatically to beds for a specific period of time while preserving their medical requirements and their preferences. In this paper, we present a novel solution to the PAS problem using the harmonysearch (HS) algorithm. We tailor the HS to solve the PAS problem by distributing patients to beds randomly in the harmony memory (HM) while respecting all hard constraints. The proposed algorithm uses five neighborhood strategies in the pitch adjustment stage. This technique helps in increasing the variations of the generated solutions by exploring more solutions in the search space. The PAS standard benchmark datasets are used in the evaluation. Initially, a sensitivity analysis of the HS algorithm is studied to show the effect of its control parameters on the HS performance. The proposed method is also compared with nine methods: non-linear great deluge (NLGD), simulated annealing with hyper-heuristic (HH-SA), improved with equal hyperheuristic (HH-IE), simulated annealing (SA), tabu search (TS), simple random simulated annealing with dynamic heuristic (DHS-SA), simple random improvement with dynamic heuristic (DHS-OI), simple random great deluge with dynamic heuristic (DHS-GD), and biogeography-based optimization (BBO). The proposed HS algorithm is able to produce comparably competitive results when compared with these methods. This proves that the proposed HS is a very efficient alternative to the PAS problem, which can be efficiently used to solve many scheduling problems of a large-scale data.
Two-sided assembly lines are designed to produce large and complex products, where workers can perform on both sides at the same time. This paper establishes a mathematical model for the multi-objective two-sided asse...
详细信息
Two-sided assembly lines are designed to produce large and complex products, where workers can perform on both sides at the same time. This paper establishes a mathematical model for the multi-objective two-sided assembly line balancing problems with additional constraints (MOATALBP). The model considers both workers skills and the balance of the assembly line, aiming to maximize efficiency and minimize workers cost and smoothness index. A harmony search algorithm (HS) based on Pareto entropy (PE-MHS) is proposed to solve MOATALBP. The difference entropy of Pareto solutions is employed to adjust the algorithm parameters to enhance the optimization ability of PE-MHS. Moreover, a fine-tuning operation combining insertion and inverse sequence is utilized to avoid the algorithm from falling into local optima. Ultimately, non-dominated sorting ensures a set of well-distributed Pareto solutions. The experimental results of different problems indicate that the proposed algorithm can achieve better solutions than three classical algorithms (NSGAII, SPEA2 and HS) for the MOATALBP.
The harmonysearch (HS) algorithm, inspired by the improvisational process of musicians, offers a novel and effective approach to optimization problems. This paper presents an application designed to solve a wide rang...
详细信息
The harmonysearch (HS) algorithm, inspired by the improvisational process of musicians, offers a novel and effective approach to optimization problems. This paper presents an application designed to solve a wide range of minimization problems using the HS algorithm. By simulating the creative exploration and refinement seen in musical harmonies, the HS algorithm efficiently navigates complex solution landscapes, delivering high accuracy and computational efficiency. Illustrated through various optimization examples, this tool showcases the versatility and power of the HS algorithm in addressing linear, non-linear, and discrete models. Our work highlights the practical utility of bio-inspired algorithms in solving real -world problems, providing a userfriendly platform for researchers and engineers to harness the potential of the HS algorithm in diverse fields.
Using hybrid renewable energy systems is a smart choice for reducing the carbon emitted by power plants. It also helps climate change mitigation and global warming, leading to universal health for humans and the envir...
详细信息
Using hybrid renewable energy systems is a smart choice for reducing the carbon emitted by power plants. It also helps climate change mitigation and global warming, leading to universal health for humans and the environment. The efficiency of these systems depends on choosing the right combination of renewable sources, their sizes, and proper scheduling of the generating units. This paper suggests an optimisation method for sizing a geothermal/PV/wind/diesel system, both in off-grid and grid-connected configurations. In the grid-connected mode, the possibility of selling surplus energy generated by renewable resources to the network is considered. Investigations reveal that without accurate control of geothermal reservoirs, they may become depleted. In this study, a strategy is suggested for unit commitment;moreover, the harmony search algorithm is used to find the optimal size of the hybrid system in both configurations. The effectiveness of the proposed approach is represented by simulating an HRES for Ferdows/Iran.
An adaptive harmony search algorithm utilizing differential evolution and opposition based learning (AHS-DE-OBL) is proposed to overcome the drawbacks of the harmonysearch (HS) algorithm, such as its low fine-tuning ...
详细信息
An adaptive harmony search algorithm utilizing differential evolution and opposition based learning (AHS-DE-OBL) is proposed to overcome the drawbacks of the harmonysearch (HS) algorithm, such as its low fine-tuning ability, slow convergence speed, and easily falling into a local optimum. In AHS-DE-OBL, three main innovative strategies are adopted. First, inspired by the differential evolution algorithm, the differential harmonies in the population are used to randomly perturb individuals to improve the fine-tuning ability. Then, the search domain is adaptively adjusted to accelerate the algorithm convergence. Finally, an opposition-based learning strategy is introduced to prevent the algorithm from falling into a local optimum. The experimental results show that the proposed algorithm has a better global search ability and faster convergence speed than other selected improved harmony search algorithms and selected metaheuristic approaches.
Image segmentation is a process of portion image into regions. From image segmentation schemes available, multilevel thresholding on the histogram is a highly established method. Otsu's method is a significant mul...
详细信息
Image segmentation is a process of portion image into regions. From image segmentation schemes available, multilevel thresholding on the histogram is a highly established method. Otsu's method is a significant multilevel thresholding technique, in this multiple threshold levels selected on histogram and group the pixels of an image into different regions. The optimized threshold levels computed with an Optimized technique by maximizing the inter-class variance. Methods with histograms are incapable to possess spatial details of contextual information for finding optimal threshold levels. As a remedy, a novel method proposed the Energy Curve is used instead of a histogram with Otsu's method and harmony search algorithm to compute optimized gray levels. The proposed method experimented on several benchmark images, and results compared with various optimization algorithms with histogram by Dunn Index, DB Index, SD Index, mean of fitness and PSNR, comparison clarifies that the proposed method is superior to histogram-based methods. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
暂无评论