Meta-heuristic Genetic Algorithm Research Articles

Conceptual design and optimisation of a novel hybrid device for capturing offshore wind and wave energy

AbstractThe access to the offshore wind resource in the deep sea requires the development of innovative solutions which reduce the cost of energy. Novel technologies propose the hybrid combination of wind and wave energy to improve the synergy between these technologies sharing costs, such as mooring and electrical connexion. This work proposes a novel hybrid wind and wave energy system integrating a floating offshore wind turbine with three-point absorbers wave energy converters (WECs). The WECs are an integral part of the floating structure and contribute significantly to the hydrostatic and dynamic stability of the system. Their geometry is optimised considering a cylindrical, semi-cylindrical and spherical shape for the Pantelleria case study. The cylindrical shape with the largest radius and the lowest height is the optimal solution in terms of reducing structural costs and maximising the performance of the WECs. The in-house hydrostatic stability tool and the time domain model MOST are used to optimise the WECs, with a combined meta-heuristic genetic algorithm with the Kriging surrogate model and a local Nelder–Mead optimization in the final simulations. The power of the WECs is estimated with both linear and variable motor flow hydraulic PTOs to obtain a more realistic electrical power generation. Generally, the hybrid device proved to be more competitive than the floating wind turbine alone, with a LCOE reduction up to 11%. Performance of the hybrid device can be further improved when more energetic sites are considered, as the energy generated by the WECs is higher.

An Optimized Arabic Multilabel Text Classification Approach Using Genetic Algorithm and Ensemble Learning

Multilabel classification of Arabic text is an important task for understanding and analyzing social media content. It can enable the categorization and monitoring of social media posts, the detection of important events, the identification of trending topics, and the gaining of insights into public opinion and sentiment. However, multilabel classification of Arabic contents can present a certain challenge due to the high dimensionality of the representation and the unique characteristics of the Arabic language. In this paper, an effective approach is proposed for Arabic multilabel classification using a metaheuristic Genetic Algorithm (GA) and ensemble learning. The approach explores the effect of Arabic text representation on classification performance using both Bag of Words (BOW) and Term Frequency-Inverse Document Frequency (TF-IDF) methods. Moreover, it compares the performance of ensemble learning methods such as the Extra Trees Classifier (ETC) and Random Forest Classifier (RFC) against a Logistic Regression Classifier (LRC) as a single and ensemble classifier. We evaluate the approach on a new public dataset, namely, the MAWQIF dataset. The MAWQIF is the first multilabel Arabic dataset for target-specific stance detection. The experimental results demonstrate that the proposed approach outperforms the related work on the same dataset, achieving 80.88% for sentiment classification and 68.76% for multilabel tasks in terms of the F1-score metric. In addition, the data augmentation with feature selection improves the F1-score result of the ETC from 65.62% to 68.80%. The study shows the ability of the GA-based feature selection with ensemble learning to improve the classification of multilabel Arabic text.

Bayesian optimization of ESG (Environmental Social Governance) financial investments

Financial experts seek to predict the variability of financial markets to ensure investors’ successful investments. However, there has been a big trend in finance in the last few years, which are the ESG (Economic, Social and Governance) criteria, due to the growing importance of investments being socially responsible, and because of the financial impact companies suffer when not complying with them. Consequently, creating a stock portfolio should consider not only its financial performance but compliance with ESG criteria. Portfolio optimization (PO) techniques previously applied to ESG portfolios, are all closed-form analytical ones. But the real world is rather a black box with unknown analytical expressions. Thus, in this paper we use Bayesian optimization (BO), a sequential state-of-the-art design strategy to optimize black-boxes with unknown analytical and costly-to-compute expressions, to maximize the performance of a stock portfolio under the presence of ESG criteria soft constraints incorporated into the objective function. And we compare it to two other black-box techniques widely applied for the optimization of ‘conventional portfolios’ (non-ESG ones): the metaheuristics Genetic algorithm (GA) and Simulated Annealing (SA). Although BO has many theoretical advantages over GA and SA, it has never been applied to PO. Thus, this paper investigates whether BO can be used in the ESG PO framework as an alternative and compares it with GA and SA. This is the research gap to which this paper responds. To show the empirical performance of BO, we carry out four illustrative experiments and find evidence of BO outperforming the baselines. Thus we add another different optimization approach to the world of ESG investing: a black-box non-heuristic optimization approach through BO. Our study is the first paper that leverages BO and ESG scores into a PO technique. This paper opens the door to many new research lines in (ESG) portfolio optimization.

A Day Ahead Demand Schedule Strategy for Optimal Operation of Microgrid with Uncertainty

A microgrid energy management system (EMS) with several generation and storage units is crucial in attaining stable and reliable operation. Optimal scheduling of energy resources in EMS becomes arduous due to uncertainty in the forecasting of intermittent renewable sources, electricity pricing, and load demand. However, with the demand response (DR) approaches the operational benefits in the EMS framework can be maximized. In order to improve the cost-effectiveness of the microgrid, a novel day-ahead energy management strategy is proposed for optimal energy allocation of the distributed generators with environmental consideration. An incentive load control-based demand response program is developed to improve the operational results. The forecasting uncertainties are handled using probability-based Hong’s 2 m approximation method. The suggested approach uses a metaheuristic genetic algorithm (GA) to solve the constrained convex problem in determining optimal load shifting. Incentive pricing is developed to adapt to the demand shifting for the benefit of the customers and utility operators. Two case studies with grid-connected and islanded modes are studied to assess the strategy. Results indicate that the proposed technique reduces the overall cost fitness by 12.28% and 18.91% in the two cases, respectively. The consistency in operational parameters with popular methods confirms the effectiveness and robustness of the method for day-ahead energy management.

When Fairness Meets Consistency in AHP Pairwise Comparisons

We propose introducing fairness constraints to one of the most famous multi-criteria decision-making methods, the analytic hierarchy process (AHP). We offer a solution that guarantees consistency while respecting legally binding fairness constraints in AHP pairwise comparison matrices. Through a synthetic experiment, we generate the comparison matrices of different sizes and ranges/levels of the initial parameters (i.e., consistency ratio and disparate impact). We optimize disparate impact for various combinations of these initial parameters and observed matrix sizes while respecting an acceptable level of consistency and minimizing deviations of pairwise comparison matrices (or their upper triangles) before and after the optimization. We use a metaheuristic genetic algorithm to set the dually motivating problem and operate a discrete optimization procedure (in connection with Saaty’s 9-point scale). The results confirm the initial hypothesis (with 99.5% validity concerning 2800 optimization runs) that achieving fair ranking while respecting consistency in AHP pairwise comparison matrices (when comparing alternatives regarding given criterium) is possible, thus meeting two challenging goals simultaneously. This research contributes to the initiatives directed toward unbiased decision-making, either automated or algorithm-assisted (which is the case covered by this research).

A Genetic algorithm for Objective formulation effect on the shortfall of retirees in developing countries: a case study in Iran

The certainty about retirement income is dependent on the longevity and the selected investment policy by individuals during their working years. Attention to longevity and investment risks is of high significance in making enough income for retirees. In this research, the impact of the objective formulation selection on investment decisions has been investigated. Two functions including terminal wealth objective formulation and retirement income objective formulation are applied to investigate these decisions. Based on the investment alternatives, 5 asset classes including Equity, Certificate of deposit (cash), real estate investment trust (REIT), gold coin, and foreign exchange have been selected for investment. According to the complexity of modeling in the aforementioned functions, the Metaheuristic Genetic algorithm has been used. The results are indicative of the importance of objective formulation selection. The retirement income objective function has the nature of more wealth accumulation and more control over economic and market turbulences through higher cooperation in investment and as a result, it has been recommended as the optimal function.

DARCAS: Dynamic Association Regulator Considering Airtime Over SDN-Enabled Framework

The massive influx of mobile devices and their increasing use in recent years have resulted in the overprovision of access points (APs) in networks. Unlike in residential environments, network administrators in enterprises and universities make every endeavor to enhance the user experience (UX) of WiFi networks where the network dynamics (e.g., traffic load and user mobility) are usually unexpected. To this end, an existing mechanism for WiFi association is client driven, i.e., users associate themselves to the AP with higher signal strength. However, they still incur dissatisfaction due to the insufficient available bandwidth. To cope with this in a centralized manner, we propose DARCAS, a software-defined network (SDN)-enabled WiFi framework for association regulation. DARCAS adopts a notion of bandwidth satisfaction ratio (BSR), which is closely related to UX. It maximizes the aggregated network throughput while satisfying the BSR of each user with sufficient airtime (i.e., channel occupancy time) provision. We use this idea in a metaheuristic genetic algorithm called DARCAS-GA, which effectively finds the suboptimal association distribution of the maximum BSR in polynomial time. We implement the DARCAS system on off-the-shelf wireless routers and an SDN controller. We report real-life experimental results in the considered scenarios and conduct extensive simulations on the NS-3 simulator to examine its performance with scalability. With fine-tuned settings, DARCAS exhibits up to 80% of the BSR gain compared to existing solutions.

Solving the Container Relocation Problem by Using a Metaheuristic Genetic Algorithm

Maritime transport is the backbone of international trade of goods. Therefore, seaports are of great importance for maritime transport. Container transport plays an important role in maritime transport and is increasing year by year. Containers transported to a container terminal are stored in container yards side by side and on top of each other, forming blocks. If a container that is not on top of the block has to be retrieved, the containers that are above the required container must be relocated before the required container is retrieved. These additional container relocations, which block the retrieval of the required container, slow down the entire retrieval process. The container relocation problem, also known as the block relocation problem, is an optimization problem that involves finding an optimal sequence of operations for retrieving blocks (containers) from a container yard in a given order, minimizing additional relocations of blocking containers. In this paper, the focus is on the two-dimensional, static, offline and the restricted container relocation problem of real-size yard container bays. A new method for resolving the container relocation problem that uses a genetic algorithm is proposed to minimize the number of relocations within the bay. The method is evaluated on well-known test instances, and the obtained results are compared with the results of various relevant models for resolving the container relocation problem. The results show that the proposed method achieves the best or the second-best result for each test instance within the test set.

Optimum Test Suite Using Fault-Type Coverage-Based Ant Colony Optimization Algorithm

Software Product Lines(SPLs) covers a mixture of features for testing Software Application Program(SPA). Testing cost reduction is a major metric of software testing. In combinatorial testing(CT), maximization of fault type coverage and test suite reduction plays a key role to reduce the testing cost of SPA. Metaheuristic Genetic Algorithm(GA) do not offer best outcome for test suite optimization problem due to mutation operation and required more computational time. So, Fault-Type Coverage Based Ant Colony Optimization(FTCBACO) algorithm is offered for test suite reduction in CT. FTCBACO algorithm starts with test cases in test suite and assign separate ant to each test case. Ants elect best test cases by updating of pheromone trails and selection of higher probability trails. Best test case path of ant with least time are taken as optimal solution for performing CT. Hence, FTCBACO Technique enriches reduction rate of test suite and minimizes computational time of reducing test cases efficiently for CT.

Mathematical Model of a Thermophilic Anaerobic Digestion for Methane Production of Wheat Straw

This paper presents a newly created mathematical model of thermophilic anaerobic digestion of wheat straw carried out in a 2 dm3 bioreactor for methane production. Two batch processes, with 30 mL/dm3 and 35 mL/dm3 organic load, are carried out—one set for parameter identification and one set for model verification. The identification of model parameter values is based on dynamical experiments. It is fulfilled using two different techniques: deterministic sequential quadratic programming algorithm and metaheuristic genetic algorithm. Verification of the developed mathematical models is conducted based on the different data sets of the process. Both models predict the set of the experimental data for all considered process variables well. Genetic algorithm visually fits the data with a higher degree of accuracy, as confirmed by the numerical results for the objective function value.

An extended stage-wise superstructure for heat exchanger network synthesis with intermediate placement of multiple utilities

The heat exchanger network (HEN) synthesis methodologies based on mathematical programming are recognized to be the significant methods to improve energy utilization efficiency and achieve a prominent techno-economic performance. As impacted by the combinatorial and nonconvex nature in the mathematical model, the problem of HEN synthesis may be easily trapped into suboptimal solutions. In this study, an extended stage-wise superstructure with intermediate placement of multiple utilities was presented. The heaters and coolers can be put in different stream split branches at intermediate stages, as opposed to the traditional allocation at stream ends. Such an extended superstructure yielded a more complex mathematical model compared to the stage-wise superstructure. To solve the model, the solution approach based on Genetic Algorithm (GA) was adopted. The local optimizing strategy, the adaptive scheme, the elite strategy and the structure identification and structure control strategy were introduced to enhance the efficiency and improve the search ability of the GA. Finally, the analysis of four examples verifies the validity of the presented meta-heuristics GA with optimization strategies, demonstrating that the proposed method can solve the complex model with acceptable computational effort and obtain better solutions with lower total annual costs than those reported in previous works.

The problem of production-distribution under uncertainty based on Vendor Managed Inventory

In this paper, a problem of managed inventory by the vendor in the production-distribution supply chain is presented based on the scenario. The main purpose of presenting the model of maximizing producer profit in a three-level supply chain network consisting of various strategic and tactical decisions under uncertainty. Due to the nonlinearity and NP-Hardness of the problem, meta-heuristic genetic algorithms, Whale optimization algorithm and league champions algorithm have been used. The results of problem solving show the high efficiency of meta-heuristic algorithms compared to accurate methods in solving the above model. So that the maximum percentage of relative differences between the methods mentioned with GAMS is less than 1%.Also, by solving the sample problems in larger sizes, it was observed that the league champions algorithm has the highest efficiency in terms of achieving the optimal value of the target function in a shorter time than the other algorithms used, with a useful weight of 0.998.

A State-of-the-art Model of Location, Inventory, and Pricing Problem in the Closed-loop Supply Chain Network

The main objective of designing the supply chain is to increase profitability. For this reason, a state-of-the-art model of a three-echelon closed-loop supply chain is proposed that consists of the manufacturer, retailer, and collection centers. For the first time, a new separate and autonomous channel is considered in this model for the sale of Reman products aiming at increasing the manufacturer's profitability. In this model, location, inventory, and pricing of the product are also taken into consideration. Lingo software is utilized to solve nonlinear objective functions at a small scale, and metaheuristic genetic algorithms and particle swarm optimization were utilized to solve at a large scale. The research results depict that the state-of-the-art model design of the closed-loop supply chain network is credible and the optimal location of supply chain components, optimal response of product flow, and product price are determined in a proper manner. As a result, the profitability of the whole closed-loop supply chain network increased. Sensitivity analysis of mathematical model depicts that this model shows higher sensitivity to retailer replacement factor, price and purchasing power of collection centers. Also, the genetic algorithm shows better performance on a large scale in terms of response quality. On the other hand, the time required by particle swarm optimization to reach a response is far better than the genetic algorithm. Ultimately, practical suggestions for the managers are presented considering the state-of-the-art model design of a closed-loop supply chain network.

An Intelligent Security Framework Based on Collaborative Mutual Authentication Model for Smart City Networks

With the advent of smart city networks and increased utilization of vehicles, the Internet of Vehicles (IoV) has attracted more attention from researchers. But, providing security to this type of network is one of the challenging and demanding tasks in the present day. For this purpose, the conventional works developed many networking frameworks and methodologies to enhance the privacy and security of smart city systems. Still, it has the significant limitations of high complexity in algorithm design, requires more time consumption for processing, reduced maintenance, and does not require proper authentication verification. Therefore, the primary purpose of this work is to develop a new security model for smart city networks using a combination of methodologies. Here, the Collaborative Mutual Authentication (CMA) mechanism is used to validate the identity of users based on the private key, public key, session key, and generated hash function. In addition, the Meta-heuristic Genetic Algorithm – Random Forest (MGA-RF) technique is deployed to detect the attacks in the network, ensuring the security of the smart city. During an evaluation, the proposed authentication-based security mechanism’s performance is validated using various parameters, and the results are compared with the recent state-of-the-art models.

Long Short Term Memory Water Quality Predictive Model Discrepancy Mitigation Through Genetic Algorithm Optimisation and Ensemble Modeling

A predictive long short-term memory (LSTM) model developed on a particular water quality dataset will only apply to the dataset and may fail to make an accurate prediction on another dataset. This paper focuses on improving LSTM model tolerance by mitigating discrepancies in model prediction capability that arises when a model is applied to different datasets. Two predictive LSTM models are developed from the water quality datasets, Baffle and Burnett, and are optimised using the metaheuristic genetic algorithm (GA) to create hybrid GA-optimised LSTM models that are subsequently combined with a linear weight-based technique to develop a tolerant predictive ensemble model. The models successfully predict river water quality in terms of dissolved oxygen concentration. After GA-optimisation, the RMSE values of the Baffle and Burnett models decrease by 42.42% and 10.71%, respectively. Furthermore, two ensemble models are developed from the GA-hybrid models, namely the average ensemble and the optimal weighted ensemble. The GA-Baffle RMSE values decrease by 5.05% for the average ensemble and 6.06% for the weighted ensemble, and the GA-Burnett RMSE values decrease by 7.84% and 8.82%, respectively. When tested on unseen and unrelated datasets, the models make accurate predictions, indicating the applicability of the models in domains outside the water sector. The consistent and similar performance of the models on any dataset illustrates the successful mitigation of discrepancies in the predictive capacity of individual LSTM models by the proposed ensemble scheme. The observed model performance highlights the datasets on which the models could potentially make accurate predictions.

Low-Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks

Collaborative mobile crowdsourcing (CMCS) allows entities, e.g., local authorities or individuals, to hire a team of workers from the crowd of connected people, to execute complex tasks. In this article, we investigate two different CMCS recruitment strategies allowing task requesters to form teams of socially connected and skilled workers: 1) a platform-based strategy where the platform exploits its own knowledge about the workers to form a team and 2) a leader-based strategy where the platform designates a group leader that recruits its own suitable team given its own knowledge about its social network (SN) neighbors. We first formulate the recruitment as an integer linear program (ILP) that optimally forms teams according to four fuzzy-logic-based criteria: 1) level of expertise; 2) social relationship strength; 3) recruitment cost; and 4) recruiter’s confidence level. To cope with NP-hardness, we design a novel low-complexity CMCS recruitment approach relying on graph neural networks (GNNs), specifically graph embedding and clustering techniques, to shrink the workers’ search space and afterwards, exploiting a metaheuristic genetic algorithm to select appropriate workers. Simulation results applied on a real-world data set illustrate the performance of both proposed CMCS recruitment approaches. It is shown that our proposed low-complexity GNN-based recruitment algorithm achieves close performances to those of the baseline ILP with significant computational time saving and ability to operate on large-scale mobile crowdsourcing platforms. It is also shown that compared to the leader-based strategy, the platform-based strategy recruits a more skilled team but with lower SN relationships and higher cost.

An Optimal Execution of Composite Service in Decentralized Environment

It is important for service-oriented architectures to consider about how the composition of web services affects business processes. For instance, a single web service may not have been adequate for most complex business operations, needing the use of multiple web services. This paper proposed a novel technique for optimal partitioning and execution of the services using a decentralized environment. The proposed technique is designed and developed using a genetic algorithm with multiple high task allocations on a single server. We compared three existing techniques, including meta-heuristic genetic algorithm, heuristics like Pooling-and-Greedy-Merge (PGM) technique, and Merge-by-Define-Use (MDU) technique, to a simulation of Business Process Execution Language (BPEL) partition using genetic algorithm through multiple high tasks allocation to single server node. The proposed technique is practical and advantageous. In terms of execution time, number of server requests, and throughput, the proposed technique outperformed the existing GA, PGM, and MDU techniques.

Sustainable planning and decision-making model for sugarcane mills considering environmental issues

Many companies and organizations are pursuing “carbon footprint” projects to estimate their own contribution due to growing concerns about global climate change and carbon emissions. Measures such as carbon taxes are the most powerful means of dealing with the threats of climate change. In recent years, researchers have shown a particular interest in modelling supply chain networks under this scheme. Disorganized disposal of by-products from sugarcane mills is the inspiration of this research. In order to connect the problem with the real world, the proposed sustainable sugarcane supply chain network considers carbon taxes on the emission from industries and during transportation of goods. The presented mixed-integer linear programming modelling is a location-allocation problem and, due to the inherent complexity, it is considered a Non-Polynomial hard (NP-hard) problem. To deal with the model, three superior metaheuristics Genetic Algorithm (GA), Simulated Annealing (SA), Social Engineering Optimizer (SEO) and hybrid methods based on these metaheuristics, namely, Genetic-Simulated Annealing (GASA) and Genetic-Social Engineering Optimizer (GASEO), are employed. The control parameters of the algorithms are tuned using the Taguchi approach. Subsequently, one-way ANOVA is used to elucidate the performance of the proposed algorithms, which compliments the performance of the proposed GASEO.

Element-Based Multi-Objective Optimization Methodology Supporting a Transportation Asset Management Framework for Bridge Planning and Programming

The U.S. Moving Ahead for Progress in the 21st Century Act (MAP-21) mandates the development of a risk-based transportation asset management plan and the use of a performance-based approach in transportation planning and programming. This paper introduces a systematic element-based multi-objective optimization (EB-MOO) methodology integrated into a goal-driven transportation asset management framework to improve bridge management and support state departments of transportation with their transition efforts to comply with these MAP-21 requirements. The methodology focuses on the bridge asset class and is structured around five modules: data processing, improvement, element-level optimization, bridge-level optimization, and network-level optimization modules. It relies on a leading-edge forecasting model, three separate screening processes (i.e., the element deficiency, alternative feasibility, and solution superiority screening processes) to overcome computer memory and processing time limitations, and a simulation arrangement to generate life-cycle alternatives (series of improvement actions). Additionally, the EB-MOO methodology consists of three levels of optimization assessment based on the Pareto optimality concept: element-level, bridge-level, and network-level (following either a top-down or bottom-up approach). A robust metaheuristic genetic algorithm handles the different multi-objective optimization problems. A prototyping tool was developed for the implementation of the methodology through several examples of unconstrained and constrained (by budget, performance, or both) scenarios. Results reveal the capability of the methodology to generate Pareto optimal or near-optimal solutions, predict performance, and determine funding requirements and short- and long-term intervention strategies detailed at the bridge-element level for planning and programming. The EB-MOO methodology can also be expanded to accommodate other asset classes or modes.

Applying hybrid artificial algorithms to the estimation of river flow: a case study of Karkheh catchment area

This study was conducted on the Karkheh catchment area in the country of Iran on the basis of data from four stations of Cham Anjir, Kashkan, Pole-zal, and Jologir. The data were monthly collected for the years from 1997 to 2017, from which 70% were used for calibration and 30% for test validation. Artificial neural networks (ANNs) were used in the prediction of river flow. To optimize the weight coefficients of the network, the meta-heuristic genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), harmonic balance algorithm (HB), and algorithm of the innovative gunner (AIG) were employed. To assess the modeling performance of the statistic parameters, the coefficient of determination (R2), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE), and percentage of bias (PBIAS) were adopted. The achieved best input combination was different for each algorithm. In summary, variables with lower correlations had poorer performances. Hybrid algorithms improved the predicting power of the considered independent model. The results showed that for the studied four stations, among the meta-heuristic algorithms, AIG had the highest predictive power correlation coefficient R2 = 0.985–0.995, root mean square error (RMSE) = 0.036–0.057 m3/s, mean absolute error (MAE) = 0.017–0.036 m3/s, Nash-Sutcliffe coefficient (NSE) = 0.984–0.994, and bias = 0.008–0.024 and had the best performance in estimating the daily flow of the river.