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Suitability Assessment of Cage Fish Farming Location in Reservoirs through Neural Networks-Based Remote Sensing Analysis

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REMOTE SENSING 2024年第2期16卷

作者： Sedighkia, Mahdi Datta, Bithin Australian Natl Univ ICEDS Canberra 2601 Australia Australian Natl Univ MSI Canberra 2601 Australia James Cook Univ Coll Sci Townsville 4811 Australia

The present study evaluates the application of different artificial intelligence methods associated with remote sensing data processing for assessing water quality parameters, with a focus on fish cage farming in the reservoirs. Three AI methods were utilized including 1-optimal artificial neural network (ONN), 2-adaptive neuro fuzzy inference system in which a hybrid algorithm was used for the training process (ANFIS) and 3-coupled evolutionary algorithm-adaptive neuro fuzzy inference system in which particle swarm optimization was utilized in the training process (EA-ANFIS). Three critical water quality parameters for cage fish farming were selected consisting of water temperature, dissolved oxygen (DO) and total dissolved solids (TDS). Moreover, two measurement indices, the Nash-Sutcliffe model efficiency coefficient (NSE) and root mean square error (RMSE), were utilized to assess the predictive skills of the data driven models. Based on the results in the case study, EA-ANFIS is the best method to simulate water temperature and DO in the reservoir by the remote sensing technique. Furthermore, the ANFIS-based model is the best method to simulate TDS. According to the results in the case study, utilizing the spectral images might not be reliable to simulate DO concentration in the reservoirs. However, the images are robust to simulate water temperature as well as TDS concentration.

关键词： remote sensing water quality artificial neural network neuro fuzzy inference system evolutionary algorithms

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An assets maintenance-workforce planning problem under uncertainty: A chance constraints assisted simulation-optimization approach

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SIMULATION MODELLING PRACTICE AND THEORY 2024年 130卷

作者： Akl, Amany M. Rahman, Humyun Fuad Chakrabortty, Ripon K. El Sawah, Sondoss Univ New South Wales Capabil Syst Ctr Sch Syst & Comp Northcott Dr Campbell ACT South Africa Newcastle Univ Business Sch Barrack Rd Newcastle Upon Tyne NE1 4SE England UNSW Canberra ADFA Sch Syst & Comp Northcott Dr Canberra ACT 2612 Australia Univ Canberra Sch Informat Technol & Syst 11 Kirinari St Canberra ACT 2617 Australia Cardiff Metropolitan Univ Cardiff Sch Management Western Ave Cardiff CF5 2YB Wales

Preventive and corrective maintenance -workforce planning for assets is a growing area of research, given the presence of uncertainty. We model a Simulation -Optimization framework to solve this challenging large-scale maintenance -workforce planning problem while considering its stochastic nature inside both the simulation and optimization models. A holistic simulation model is proposed through multiple modules to imitate different aspects of the maintenance process, i.e., spare facilities/storage and workforce. The workforce module represents technicians' types, skills and levels, and employment life -cycle, including recruitment, promotion, and separation/retirement. The present uncertainty in the simulation model demands its corresponding optimization model to cope with the nature of the problem and guarantee a degree of confidence level of the optimal decisions. Therefore, a Chance Constraints Programming (CCP) method is integrated with an evolutionary optimization algorithm to limit the risk level of violating the random constraints while minimizing the total maintenance cost. Discrete Event Simulation (DES) and Self -Adaptive Differential Evolution assisted with Chance Constraints (SADE -CC) algorithm are coupled to solve the current problem. The optimization model is proposed to optimize the preventive maintenance frequency and workforce planning problem while implicitly considering corrective maintenance in the simulation model. The Taguchi method performs scenario and risk analysis for multiple CC probability distributions (i.e., Weibull and Normal), confidence levels, and critical parameter combinations. The model's sensitivity analysis and managerial insights are also discussed.

关键词： evolutionary algorithms Chance constraints Assets maintenance Workforce planning Maintenance scheduling Simulation-optimization

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Optimization of Distributed Energy Resources Operation in Green Buildings Environment

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SENSORS 2024年第14期24卷 4742页

作者： Ali, Safdar Hayat, Khizar Hussain, Ibrar Khan, Ahmad Kim, Dohyeun Univ Lahore Dept Software Engn Main Campus Lahore 54590 Pakistan Shinawatra Univ Fac Engn & Informat Technol Bangtoey 12160 Pathum Thani Thailand COMSATS Univ Islamabad Dept Comp Sci Abbottabad Campus Abbottabad 22060 Pakistan Jeju Natl Univ Dept Comp Engn Jejusi 63243 Jeju Do South Korea

Without a well-defined energy management plan, achieving meaningful improvements in human lifestyle becomes challenging. Adequate energy resources are essential for development, but they are both limited and costly. In the literature, several solutions have been proposed for energy management but they either minimize energy consumption or improve the occupant's comfort index. The energy management problem is a multi-objective problem where the user wants to reduce energy consumption while keeping the occupant's comfort index intact. To address the multi-objective problem this paper proposed an energy control system for a green environment called PMC (Power Management and Control). The system is based on hybrid energy optimization, energy prediction, and multi-preprocessing. The combination of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) is performed to make a fusion methodology to improve the occupant comfort index (OCI) and decrease energy utilization. The proposed framework gives a better OCI when compared with its counterparts, the Ant Bee Colony Knowledge Base framework (ABCKB), GA-based prediction framework (GAP), Hybrid Prediction with Single Optimization framework (SOHP), and PSO-based power consumption framework. Compared with the existing AEO framework, the PMC gives practically the same OCI but consumes less energy. The PMC framework additionally accomplished the ideal OCI (i-e 1) when compared with the existing model, FA-GA (i-e 0.98). The PMC model consumed less energy as compared to existing models such as the ABCKB, GAP, PSO, and AEO. The PMC model consumed a little bit more energy than the SOHP but provided a better OCI. The comparative outcomes show the capability of the PMC framework to reduce energy utilization and improve the OCI. Unlike other existing methodologies except for the AEO framework, the PMC technique is additionally confirmed through a simulation by controlling the indoor environment using actuators, such as fan, light

关键词： energy management evolutionary algorithms green buildings occupants comfort index energy resources prediction

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A Novel Hybrid Multi-Objective Optimization Algorithm and Its Application to Designs of Electromagnetic Devices

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IEEE TRANSACTIONS ON MAGNETICS 2025年第2期61卷

作者： Li, Yilun Xie, Zhengwei Yang, Shiyou Ren, Zhuoxiang Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Sorbonne Univ Grp Elect & Elect Engn Paris Paris PARIS France

In this article, a novel hybrid multi-objective optimization (MOO) algorithm is proposed by combining an improved sparrow search algorithm (SSA) with an improved non-dominated sorting genetic algorithm (NSGA-II). The original SSA is improved by the introduction of population updating mechanism of moth-flame optimization (MFO) algorithm and by adopting adaptive mutation;meanwhile, NSGA-II is enhanced by using Latin hypercube sampling and dynamical selection mechanism of crossover and mutation operators. The performance of the proposed hybrid algorithm is verified using standard test functions and it is applied to the multi-objective optimal designs of TEAM22 benchmark problem and topology optimization problem of an electromagnetic actuator prototype. Numerical results demonstrate the effectiveness and superiority of the proposed algorithm.

关键词： Optimization Heuristic algorithms Polynomials Diversity reception Sorting Search problems Electromagnetic devices Hypercubes Flowcharts Fires evolutionary algorithms multi-objective optimization (MOO) TEAM22 benchmark topology optimization

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The Compact Genetic Algorithm Struggles on Cliff Functions

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ALGORITHMICA 2025年第4期87卷 507-536页

作者： Neumann, Frank Sudholt, Dirk Witt, Carsten Univ Adelaide Sch Comp & Math Sci Optimisat & Logist Adelaide SA 5005 Australia Univ Passau Fac Comp Sci & Math D-94032 Passau Germany Tech Univ Delnmark DTU Compute DK-2800 Lyngby Denmark

Estimation of distribution algorithms (EDAs) are general-purpose optimizers that maintain a probability distribution over a given search space. This probability distribution is updated through sampling from the distribution and a reinforcement learning process which rewards solution components that have shown to be part of good quality samples. The compact genetic algorithm (cGA) is a non-elitist EDA able to deal with difficult multimodal fitness landscapes that are hard to solve by elitist algorithms. We investigate the cGA on the Cliff function for which it was shown recently that non-elitist evolutionary algorithms and artificial immune systems optimize it in expected polynomial time. We point out that the cGA faces major difficulties when solving the Cliff function and investigate its dynamics both experimentally and theoretically. Our experimental results indicate that the cGA requires exponential time for all values of the update strength 1/K. We show theoretically that, under sensible assumptions, there is a negative drift when sampling around the location of the cliff. Experiments further suggest that there is a phase transition for K where the expected optimization time drops from n Theta(n)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n<^>{\Theta (n)}$$\end{document} to 2 Theta(n)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2<^>{\Theta (n)}$$\end{document}.

关键词： Estimation-of-distribution algorithms Compact genetic algorithm evolutionary algorithms Running time analysis Theory

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Large population sizes and crossover help in dynamic environments

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NATURAL COMPUTING 2024年第1期23卷 115-129页

作者： Lengler, Johannes Meier, Jonas Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland

Dynamic linear functions on the boolean hypercube are functions which assign to each bit a positive weight, but the weights change over time. Throughout optimization, these functions maintain the same global optimum, and never have defecting local optima. Nevertheless, it was recently shown [Lengler, Schaller, FOCI 2019] that the (1 + 1)-evolutionary Algorithm needs exponential time to find or approximate the optimum for some algorithm configurations. In this experimental paper, we study the effect of larger population sizes for dynamic binval, the extreme form of dynamic linear functions. We find that moderately increased population sizes extend the range of efficient algorithm configurations, and that crossover boosts this positive effect substantially. Remarkably, similar to the static setting of monotone functions in [Lengler, Zou, FOGA 2019], the hardest region of optimization for (mu + 1)-EA is not close the optimum, but far away from it. In contrast, for the (mu + 1)-GA, the region around the optimum is the hardest region in all studied *** check and confirm the inserted city name is correctly ***.

关键词： evolutionary algorithms Genetic algorithms Crossover Population size Dynamic linear functions Dynamic binval Degenerate population drift Simulations

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Fitness function with two rankings embedded in a push and pull search framework for constrained multi-objective optimisation problems

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2024年第3期24卷 164-175页

作者： Li, Kangshun Xu, Jiaxin Xie, Shumin Wang, Hui South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China Dongguan City Univ Sch Artificial Intelligence Dongguan 523109 Peoples R China Shenzhen Inst Informat Technol Sch Software Engn Shenzhen 518055 Peoples R China

Optimising objectives and satisfying constraints present significant challenges in solving constrained multi-objective optimisation problems. In this paper, we propose an algorithm that incorporates the push-and-pull search framework and a two-ranking fitness function named ToR-PPS. The algorithm is divided into three stages: the push stage, transitional stage, and pull stage. In the push stage, the population is directed toward the unconstrained Pareto front, without consideration of constraints. In the transitional stage, a diversity expansion strategy is proposed to optimise the diversity of the population. In the pull stage, the fitness function with two rankings is utilised to pull the population toward the constrained Pareto front. Experiments are conducted to compare the algorithm with five state-of-the-art constrained multi-objective optimisation evolutionary algorithms on two benchmark suites. The results clearly illustrate the superiority and efficiency of the algorithm.

关键词： constrained multi-objective optimisation problems CMOPs evolutionary algorithms push and pull search framework transitional stage diversity

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Directional cooperative networks

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NEUROCOMPUTING 2024年 593卷

作者： Ito, Yasuaki Nguyen, Le-Minh Japan Adv Inst Sci & Technol Grad Sch Informat Sci 1-1 Asahidai Nomi Ishikawa 9231292 Japan

Although various machine learning methods have been proposed for industrial applications, there are not many examples of their application in some industrial sectors. Data collected within organizations are inconsistently formatted and expensive to annotate, resulting in insufficient datasets for training. This hinders the widespread use of machine learning methods. The challenge is to address the need for more performance of machine learning models due to the lack of available data. We address this problem by introducing Directed Cooperative Networks (DCNs). This approach addresses the lack of data by connecting two neural networks with a function that evaluates the output of Generator. Estimator, the one of the networks, acts as an approximate function of the evaluation function, assisting Generator to output a product that yields the desired evaluation function value. When the networks are sufficiently trained, Estimator's output approaches the output of the evaluation function, and Generator will produce products with the desired attributes. A data -independent method is the evolutionary algorithm (EA). Since EA is optimized by feedback from the environment, good results can be obtained by using the evaluation function of the product as its environment. However, its effectiveness decreases as the combination of product components becomes more complex. One of the outstanding properties of DCN is its potential to outperform EA on complex problems because it learns using the gradient of the search field. DCN does not require rewriting the evaluation function into a backpropagatable form, even if the function is complex. Using a neural network that behaves as an approximate function of the evaluation function, the gradient descent method can be applied even if the evaluation function is non -differentiable. To validate the effectiveness of the proposed method, DCN is applied to a molecular search task and analyzed in comparison with other approaches. This study aims

关键词： Unsupervised learning Regression model Neural networks evolutionary algorithms

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Global Optimization with Derivative-free, Derivative-based and evolutionary algorithms

Global Optimization with Derivative-free, Derivative-based a...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Bashir, Hassan A. Neville, Richard S. Univ Manchester Sch Comp Sci Manchester M13 9PL Lancs England

ISBN: (纸本)9781479938407

This paper investigates global optimization methods from the perspective of population-based and restarted point-based heuristics. We examine the performance of a standard evolutionary computation (EC) methodology, a derivative-based sequential quadratic programming (SQP) algorithm and a novel derivative-free stochastic coordinate ascent (SCA) algorithm. All methods are analyzed by random sampling of the feasible search space. A comparison was made to evaluate the three algorithms, in the light of newly updated IEEE CEC2013 benchmarks, on a set of multimodal and composite test cases. Results revealed that while the standard EC algorithm is generally more robust, on the basis of convergence efficiency both the restarted SCA and SQP algorithms have shown remarkable performance on some of these benchmarks. The results further suggest that depending on the nature of the problem landscape and dimensionality the three algorithms, chosen from different optimization frameworks, perform complementary to each other.

关键词： evolutionary computation quadratic programming sampling methods search problems stochastic programming EC methodology IEEE CEC2013 benchmarks composite test cases derivative-based SQP algorithm derivative-based algorithms derivative-based sequential quadratic programming algorithm derivative-free SCA algorithm derivative-free algorithms derivative-free stochastic coordinate ascent algorithm evolutionary algorithms evolutionary computation methodology global optimization methods multimodal test cases optimization frameworks population-based heuristics restarted point-based heuristics search space standard EC algorithm Benchmark testing evolutionary computation Genetic algorithms Optimization methods Sociology Standards

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Exploring Metaheuristic Optimization algorithms in the Context of Textual Cyberharassment: A Systematic Review

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EXPERT SYSTEMS 2025年第2期42卷

作者： Shannaq, Fatima Shehab, Mohammad Alshorman, Areej Hammad, Mahmoud Hammo, Bassam Al-Omari, Wala'a Prince Sultan Univ Coll Comp & Informat Sci Riyadh Saudi Arabia Amman Arab Univ Coll Informat Technol Amman Jordan Al al Bayt Univ Fac Prince Al Hussein Bin Abdallah II IT Mafraq Jordan Jordan Univ Sci & Technol Fac Comp & Informat technol Irbid Jordan Univ Jordan King Abdullah II Sch Informat Technol Amman Jordan Princess Sumaya Univ Technol King Hussein Sch Comp Sci Amman Jordan

The digital landscape and rapid advancement of Information and Communication Technology have significantly increased social interactions, but it has also led to a rise in harmful behaviours such as offensive language, cyberbullying, and HS. Addressing online harassment is critical due to its severe consequences. This study offers a comprehensive evaluation of existing studies that employed metaheuristic optimization algorithms for detecting textual harassment content across social media platforms, highlighting their strengths and limitations. Using the PRISMA methodology, we reviewed and analysed 271 research papers, ultimately narrowing down the selection to 36 papers based on specific inclusion and exclusion criteria. By analysing key factors such as optimization techniques, feature engineering strategies, and dataset characteristics, we identify crucial trends and challenges in the field. Finally, we offer practical recommendations to improve the accuracy of predictive models, including adopting hybrid approaches, enhancing multilingual capabilities, and expanding models to operate effectively across various social media platforms.

关键词： abusive language cyberbullying evolutionary algorithms meta-heauristic algorithms offensive language online harassment

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