Hybrid Evolutionary Approach Research Articles

Evaluating Stakeholders’ Decisions in A Blockchain-Based Recycling Construction Waste Project: A Hybrid Evolutionary Game and System Dynamics Approach

To promote efficient construction waste recycling and reuse, a novel waste management approach based on blockchain technology was introduced to the industry. However, adopting blockchain platforms in construction waste recycling and reuse may impact the behavioral strategies of stakeholders and impede the prediction of the specific impacts of stakeholders’ decisions. Accordingly, this study addresses two primary questions: (1) What are the collaborative framework and the behavioral evolution trends of multiple stakeholders within the context of blockchain? (2) How can the behavioral strategies of multiple stakeholders be systematically coordinated to achieve efficient construction waste recycling and reuse driven by blockchain? To answer these questions, a tripartite game model combined with system dynamics was constructed. In this model, we aimed to elucidate the internal organizational framework, analyze the dynamic evolution process, and assess the influence of decisions made by multiple stakeholders at the individual level. It also offers corresponding policy recommendations for efficient construction waste recycling and reuse driven by blockchain at the system level. This study offers three innovations. First, it considers the decision-making of multiple stakeholders as an interdependent and coevolutionary process to overcome the defects of analyzing only one type of participant. Second, in contrast to the static analysis method, it employs a dynamic system approach to deeply analyze the evolving structures of blockchain-based projects. Third, it provides a theoretical framework for the practical implementation of blockchain-driven platforms in managing construction waste recycling and reuse, thus fostering effective policy development and management practices. This framework aims to promote sustainable development in construction waste recycling and reuse projects in China as well as globally.

Optimal Power Flow of Multi-objective Combined Heat and Power with Wind-Solar-Electric Vehicle-Tidal Using Hybrid Evolutionary Approach

Optimal Power Flow of Multi-objective Combined Heat and Power with Wind-Solar-Electric Vehicle-Tidal Using Hybrid Evolutionary Approach

Embedding Hybrid Evolutionary Approach for Learning-to-Rank Computation for the Selection of Features Using Machine Learning

Embedding Hybrid Evolutionary Approach for Learning-to-Rank Computation for the Selection of Features Using Machine Learning

A hybrid evolutionary approach for multi-objective unit commitment problem in power systems

The operating strategy of the power system is changing from the minimum cost to the minimum cost with a minimum emission approach as a result of growing worries about global warming and environmental change brought on by the combustion of fossil fuels. The generating scheduling problem becomes a multi-objective one as a result of the economic and emission objectives' inherent conflict. In this study, a multi-objective whale-differential evolution-genetic algorithm (WODEGA) is employed to produce an optimal solution that illustrates a potential trade-off between the unit commitment (UC) problem's emission and cost objectives. The environmental and economic dispatch problem with system and unit constraints is solved using the multi-objective WODEGA method. Main goal of research is to explore the problem of unit commitment and sub-problem of unit commitment i.e. economic dispatch.

Multi-objective combined heat and power with wind–solar–EV of optimal power flow using hybrid evolutionary approach

Multi-objective combined heat and power with wind–solar–EV of optimal power flow using hybrid evolutionary approach

OCC-MP: An optimal cluster based congestion aware technique multipath routing protocol in WSN using hybrid evolutionary techniques

Due to massive data traffic, wireless network congestion is inevitable, causing network performance deterioration and data transmission quality reduction. By adjustingthe traffic flow at the source node along the optimal path, Wireless sensor networks strive to evade and overcome congestion. Recently, numerous evolutionary approaches for congestion detection and avoidance have been presented, although they do not improve WSN performance. A hybrid evolutionary approach is attempted to avoid congestion in WSN and improve network performance. We present an optimum cluster-based congestion aware multipath routing protocol (OCC-MP). An improved atom search optimization (IASO) approach for efficient clustering is introduced initially in OCC-MP. Then the HSIPO method is used for decision making, which computes each node's trust degree. The HSIPO algorithm selects the cluster head (CH) from a group of nodes. Then a deep recurrent neural network (DRNN) is used to monitor congestion and provide congestion aware routing. Finally, multiple simulation situations like various node densities andat various simulation rounds supported our proposed OCC-MP techniqueoutperformedcurrent methodologies with respect to energy consumption, throughput, traffic load overflow, delivery ratio, and number of nodes alive.

Multiplex Community Detection in Social Networks Using a Chaos‐Based Hybrid Evolutionary Approach

Multiplex Community Detection in Social Networks Using a Chaos‐Based Hybrid Evolutionary Approach

A HYBRID DIFFERENTIAL EVOLUTION FOR NON-SMOOTH OPTIMIZATION PROBLEMS

Solving high dimentional, multimodal, non-smooth global optimization problems faces challenges concerning quality of solution, computational costs or even the impossibility of solving the problem. Evolutionary algorithms, in particular, differential evolution algorithm proved itself as good method of global optimization. On the other side, approach based on subgradient methods are good for optimizing non-smooth functions. Combination of these two approaches enables to improve the quality of the algorithm, using the best features of both methods. In this paper, a new hybrid evolutionary approach based on differential evolution and subgradient algorithm as the local search procedure is proposed. Behavior of the proposed SSGDE algorithm was studied in a numerical experiment on three groups of generated tests. Comparison of the new hybrid algorithm with the pure DE approach showed the advantage of the SSGDE. It has been experimentally established that the proposed method finds the global minimum in the best way for all considered dimensions of the problem with respect to the differential evolution method. The SSGDE algorithm showed the best results with a significant increase in the number of functions.

Optimizing Convolutional Neural Network (CNN) Models for Enhanced English-Devanagari Handwritten Character Recognition: A Hybrid Evolutionary Approach with Variable Length Genetic Algorithm

Abstract: Convolutional Neural Networks (CNNs) have gained widespread recognition in the field of computer vision, specifically for handwritten digit recognition. Despite their remarkable accuracy, CNNs entail significant computational training demands and are susceptible to local optima, necessitating innovative optimization strategies. This research introduces a novel approach to hyperparameter tuning for CNN models tailored to the recognition of English-Devanagari handwritten digits. This method combines a Hybrid Evolutionary Algorithm (HEA) with a Variable Length Genetic Algorithm (VLGA) and leverages a comprehensive dataset encompassing both English and Devanagari handwritten digits. The strategy extends the conventional paradigm by integrating a variable-length GA, facilitating systematic and adaptive tuning of critical CNN hyperparameters, including optimizer selection, learning rate, global hyperparameters, kernel size, filter count, activation functions, layer count, and pooling mechanisms. Extensive experimentation across benchmark datasets underscores the superior performance of the proposed approach when compared to traditional optimization methods. Seven key hyperparameters are the focal point of optimization efforts: learning rate, optimizer, kernel size, filter count, activation function, layer count, and pooling strategy. The results highlight the significant performance boost achieved by CNNs assisted by genetic algorithms, underscoring the effectiveness of evolutionary approaches in CNN training. Notably, experiments reveal that a population size of 27 yields optimal fitness values and average fitness scores. In the culmination of this research, the HEA-VLGA model achieves an impressive accuracy rate of approximately 99.38%. These findings unequivocally affirm the efficacy of incorporating evolutionary techniques as a potent avenue for enhancing CNN training processes.

Literature Review on Hybrid Evolutionary Approaches for Feature Selection

The efficiency and the effectiveness of a machine learning (ML) model are greatly influenced by feature selection (FS), a crucial preprocessing step in machine learning that seeks out the ideal set of characteristics with the maximum accuracy possible. Due to their dominance over traditional optimization techniques, researchers are concentrating on a variety of metaheuristic (or evolutionary) algorithms and trying to suggest cutting-edge hybrid techniques to handle FS issues. The use of hybrid metaheuristic approaches for FS has thus been the subject of numerous research works. The purpose of this paper is to critically assess the existing hybrid FS approaches and to give a thorough literature review on the hybridization of different metaheuristic/evolutionary strategies that have been employed for supporting FS. This article reviews pertinent documents on hybrid frameworks that were published in the period from 2009 to 2022 and offers a thorough analysis of the used techniques, classifiers, datasets, applications, assessment metrics, and schemes of hybridization. Additionally, new open research issues and challenges are identified to pinpoint the areas that have to be further explored for additional study.

A hybrid multi-step sensitivity-driven evolutionary polynomial regression enables robust model structure selection

Evolutionary Polynomial Regression (EPR) has found widespread application and use for model structure development in engineering and science. This hybrid evolutionary approach merges real world data and explanatory variables to generate well-structured models in the form of polynomial equations. The simple and transparent models produced by this technique enable us to explore, via sensitivity analysis, the robustness of the derived models. Yet, existing EPR frameworks do not make explicit use of sensitivity analysis in the selection of robust and high-fidelity model structures. In this paper, we develop a multi-step sensitivity-driven method which combines the strengths of differential evolution and model selection via Monte Carlo simulation to explore the input–output relationships of model structures. In the first step, our hybrid approach automatically determines the optimum number of terms of the polynomial equations. In a subsequent step, our algorithm explores the mean parametric response of each explanatory variable used in the mathematical formulation to select a final model structure. Finally, in our selection of the most robust mathematical structure, we take explicit consideration of the prediction uncertainty of the simulated output. We illustrate and evaluate our EPR method for different engineering problems involving modeling and prediction of the moisture content and creep index of soils. Altogether, our results demonstrate that the use of sensitivity analysis as an integral part of model structure search and selection will lead to robust models with high predictive ability.

A hybrid evolutionary game-theoretic and system dynamics approach for analysis of implementation strategies of green technological innovation under government intervention

Green technological innovation (GTI) is accompanied by a high level of risk and cost. Therefore, choosing the optimal strategy for its implementation has attracted the attention of decision-makers. In this regard, technological firms have two strategies: collaboration with green startups and performing independent green R&D. This paper analyzes the impact of maturity level and innovation level of green startups, as well as the competitive intensity between technological firms on their evolutionary behavior. For this purpose, an evolutionary game model was developed, and the impact of government static and dynamic intervention on the firms’ innovative behavior was investigated by the system dynamics. There are six possible stable equilibrium points. The results suggest that when the government adopts static intervention, at the same time, reducing the maturity level and increasing the innovation level of the green startups decreases technological firms' desire to collaborate with them. When the government adopts dynamic intervention, then it would be more desirable for the technological firms to collaborate with green startups. If a technological firm can increase its product sales compared to the competitor, this change will affect the evolution trend of the system. Increasing sale amounts by a technological firm leads to more own stability rate, decreasing it for its competitor.

An Opposition-Based Hybrid Evolutionary Approach for Task Scheduling in Fog Computing Network

An Opposition-Based Hybrid Evolutionary Approach for Task Scheduling in Fog Computing Network

Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution

Online media has an increasing presence on the restaurants’ activities through social media websites, coinciding with an increase in customers’ reviews of these restaurants. These reviews become the main source of information for both customers and decision-makers in this field. Any customer who is seeking such places will check their reviews first, which usually affect their final choice. In addition, customers’ experiences can be enhanced by utilizing other customers’ suggestions. Consequently, customers’ reviews can influence the success of restaurant business since it is considered the final judgment of the overall quality of any restaurant. Thus, decision-makers need to analyze their customers’ underlying sentiments in order to meet their expectations and improve the restaurants’ services, in terms of food quality, ambiance, price range, and customer service. The number of reviews available for various products and services has dramatically increased these days and so has the need for automated methods to collect and analyze these reviews. Sentiment Analysis (SA) is a field of machine learning that helps analyze and predict the sentiments underlying these reviews. Usually, SA for customers’ reviews face imbalanced datasets challenge, as the majority of these sentiments fall into supporters or resistors of the product or service. This work proposes a hybrid approach by combining the Support Vector Machine (SVM) algorithm with Particle Swarm Optimization (PSO) and different oversampling techniques to handle the imbalanced data problem. SVM is applied as a machine learning classification technique to predict the sentiments of reviews by optimizing the dataset, which contains different reviews of several restaurants in Jordan. Data were collected from Jeeran, a well-known social network for Arabic reviews. A PSO technique is used to optimize the weights of the features, as well as four different oversampling techniques, namely, the Synthetic Minority Oversampling Technique (SMOTE), SVM-SMOTE, Adaptive Synthetic Sampling (ADASYN) and borderline-SMOTE were examined to produce an optimized dataset and solve the imbalanced problem of the dataset. This study shows that the proposed PSO-SVM approach produces the best results compared to different classification techniques in terms of accuracy, F-measure, G-mean and Area Under the Curve (AUC), for different versions of the datasets.

A hybrid evolutionary algorithm approach for estimating the throughput of short reliable approximately balanced production lines

A hybrid evolutionary algorithm approach for estimating the throughput of short reliable approximately balanced production lines

An Evolutionary-Based Sentiment Analysis Approach for Enhancing Government Decisions during COVID-19 Pandemic: The Case of Jordan

The world has witnessed recently a global outbreak of coronavirus disease (COVID-19). This pandemic has affected many countries and has resulted in worldwide health concerns, thus governments are attempting to reduce its spread and impact on different aspects of life such as health, economics, education, and politics by making emergent decisions and policies (e.g., lockdown and social distancing). These new regulations influenced people’s daily life and cast significant burdens, concerns, and disparities on various population groups. Taking the wrong actions and enforcing bad decisions by some countries result in increasing the contagion rate and more catastrophic results. People start to post their opinions and feelings about their government’s decisions on different social media networks, and the data received through these platforms present a very useful source of information that affects how governments perceive and cope with the current the pandemic. Jordan was one of the top affected countries. In this paper, we proposed a decision support system based on the sentiment analysis mechanism by combining support vector machines with a whale optimization algorithm for automatically tuning the hyperparameters and performing feature weighting. The work is based on a hybrid evolutionary approach that aims to perform sentiment analysis combined with a decision support system to study people’s posts on Facebook to investigate their attitudes and feelings toward the government’s decisions during the pandemic. The government regulations were divided into two periods: the first and latter regulations. Studying public sentiments during these periods allows decision-makers in the government to sense people’s feelings, alert them in case of possible threats, and help in making proactive actions if needed to better handle the current pandemic situation. Five different versions were generated for each of the two collected datasets. The results demonstrate the superiority of the proposed Whale Optimization Algorithm & Support Vector Machines (WOA-SVM) against other metaheuristic algorithms and standard classification models as WOA-SVM has achieved 78.78% in terms of accuracy and 84.64% in term of f-measure, while other standard classification models such as NB, k-NN, J84, and SVM achieved an accuracy of 69.25%, 69.78%, 70.17%, and 69.29%, respectively, with 64.15%, 62.90%, 60.51%, and 59.09% F-measure. Moreover, when comparing our proposed WOA-SVM approach with other metaheuristic algorithms, which are GA-SVM, PSO-SVM, and MVO-SVM, WOA-SVM proved to outperform the other approaches with results of 78.78% in terms of accuracy and 84.64% in terms of F-measure. Further, we investigate and analyze the most relevant features and their effect to improve the decision support system of government decisions.

A Hybrid Evolutionary Approach Applied to the Economic Dispatch Problem with Prohibited Operating Zones and Uncertainties

In order to be able to implement a solution to a real world problem, it must be solved taking into account the uncertainties in the associated model. In fact, there is a possibility that the action of a small uncertainty on a nominal solution obtained for a real world problem may become completely meaningless in practice. This paper presents a hybrid algorithm, HEA-GA-SA, to solve the Economic Dispatch Problem with Prohibited Operating Zones and Uncertainties. HEA-GA-SA considers the worst case of parametric uncertainties. The parameter in which the uncertainties have been considered is the power of generating units. The proposed algorithm proved to be effective to solve this problem. Despite having higher computational and dispatch costs than that of the nominal optimal solutions, the robust solutions found by HEA-GA-SA do not become unfeasible in the presence of uncertainties. Therefore, they can be implemented in the real world. The experiments showed that all nominal optimal solutions analyzed (including two in literature) became infeasible in the presence of uncertainties. They violated some of the constraints imposed on the problem, such as the Prohibited Operation Zones (POZs) or the power intervals in which each generator can operate. It was also observed that the presence of POZs makes the problem more difficult and vulnerable to uncertainty.

Estimating unconfined compressive strength of unsaturated cemented soils using alternative evolutionary approaches

The use of cement as a curing agent has been widely adopted in soft soil engineering to increase the strength of soft soil. The cemented soil is gradually exposed to the air and in a natural environment becomes unsaturated. Unconfined compressive strength (UCS) of the unsaturated cemented soils is a key parameter for assessing their strength behaviour. UCS determination of unsaturated cemented soils by using laboratory methods is a complex, time-consuming, and expensive procedure due to the difficulty in suction control. This study aims to model the UCS of unsaturated cemented Wenzhou clay, i.e., capture the nonlinear relations between UCS and its influential variables including cement content (%), dry density (g/cm3) and suction (MPa) for the first time by using machine learning approach. Toward this aim, three advanced computational frameworks are developed based on hybrid evolutionary approaches in which evolutionary optimisation algorithms including genetic algorithm (GA), particle swarm optimisation (PSO) and imperialist competitive algorithm (ICA) are hybridised with artificial neural network (ANN). Results show that developed models have a great ability to mimic the nonlinear relationships between UCS and its influential variables and PSO-ANN presents the best performance among three models on the training dataset with R2=0.9888, RMSE=0.129 and VAF=97.742, and testing dataset with R2=0.9412, RMSE=0.237 and VAF=90.414. To facilitate engineering application, an engineering database for Wenzhou soft clay at different cement ratios (up to 11%), suctions (up to 300 MPa) and dry densities (1–1.5 g/cm3) is built by using the developed PSO-ANN model.

A hybrid evolutionary approach to job-shop scheduling with generic time lags

A hybrid evolutionary approach to job-shop scheduling with generic time lags

Hybrid evolutionary network architecture search (HyENAS) for convolution class of deep neural networks with applications

AbstractConvolutional Neural Networks (CNNs) and its variants are increasingly used across wide domain of applications achieving high performance measures. For high performance, application specific CNN architecture is required, hence the need for network architecture search (NAS) becomes essential. This paper proposes a hybrid evolutionary approach for network architecture search (HyENAS), and targets convolution class of neural networks. One of the significant contribution of this technique is to completely evolve the high performance network by simultaneously finding network structures and their corresponding parameters. An elegant string representation has been proposed which efficiently represents the network. The concept of sparse block evolving requisite layer wise features for dense network is deployed. This permits the network to dynamically evolve for a specific application. In comparison to the other state‐of‐art methods, the high performance of the proposed HyENAS approach is demonstrated across various benchmark data sets belonging to the domain of malariology, oncology, neurology, ophthalmology, and genomics. Further, to deploy the proposed model on lower hardware specification devices, another salient feature of the HyENAS technique is to seamlessly sift out the simpler network architecture with comparable accuracy.