Heuristic Optimization Techniques Research Articles

Study of biocapacity areas to reduce ecological footprint deficits: A case study of Turkey

Our world has had difficulty meeting humans' needs in recent years. To ensure that the world can sustain its inhabitability and self-sufficiency in terms of natural resources, it is required to make the total amount of biocapacity areas equal to or higher than the ecological footprint. An analytical study has been carried out to remedy the biocapacity deficit by utilizing this information for Turkey and then these areas are optimized with heuristic optimization techniques. As a result, Artificial Bee Colony provides better objective function results (fewer errors) compared to Particle Swarm Optimization and Global Optimization Method Based on Clustering and Parabolic Approximation in terms of minimum, maximum, average value, and standard deviation. The rates of change according to the current situation of the biocapacity areas in 2016 are 277.97 %, 30.28 %, −29.28 %, 14.97 %, and −44.85 % for cropland, grazing land, forestland, fishing grounds, and built-up land, respectively. Depending on the population growth, these rates should additionally change by 83.24 %, −0.69 %, 3.97 %, 6.22 %, and −14.24 % respectively in 2050. The developed model can be used in industry or within the frame of government development policy and thus the balance between ecological footprint and biocapacity can be kept under control.

Particle Swarm Optimization (PSO) And Evolutionary Programming (EP) Technique for Optimal Placement and Sizing DG for Minimizing Loss in IEEE-30 Bus System

In recent years, most growing nations worldwide have experienced an unavoidably rising demand for electricity, which may result in a dropping of the voltage profile and a reduction in system stability, both of which have the potential to overload the power generation system significantly. One of the ways to reduce loss system network in this study is by distributed generation (DG) allocation, which the DG optimal location and sizing are the two most significant considerations for integration of DG. Improper placement and DG sizing in the power system not only result in increased total power losses but also affect the electrical system’s capability to be function properly. This paper provides a comparative analysis of two meta- heuristic optimization techniques, that is, Particle Swarm Optimization (PSO) and Evolutionary Programming (EP), to examine the optimal placement and sizing of distributed-generation (DG) for photovoltaic systems with the aim of minimizing the losses in interconnected distributed generation, IEEE-30 bus system. The objective function of this study is minimization the power loss with considered some constraints which are voltage limits and DG sizing limits are being covers in this study. The optimization issue has been solved by working with MATLAB/m-files software. The outcomes derived from the simulation results in this study demonstrated that PSO performance of two units of DG is better compared to EP algorithm. The voltage profile was improved drastically, and the power loss was reduced as lowest as 17.4486MW in the first case, 17.4368MW in the second case, and 17.4179MW in the third case.

Epigenetik Algoritma ile Konferans Çizelgeleme

The most important of the activities where the presentations of scientific studies take place are academic conferences. The days, halls, and sessions are determined in advance to organize multidisciplinary conferences and this process is called conference scheduling. In multidisciplinary conferences, in the scheduling of presentations, the coexistence of studies belonging to the same fields in the same sessions is very important for the conference listener and the conference speaker. In this context, the multidisciplinary conference scheduling problem is considered a multi-constraint optimization problem. Multi-constraint optimization problems are solved with heuristic optimization techniques, not traditional optimization methods. In this study, the problem of conference scheduling is addressed using multidisciplinary conference data. The solution to the conference scheduling problem was realized with Genetic Algorithm (GA) and Epigenetic Algorithm (EGA) using C# programming language. In the study, experimental results obtained with GA and EGA were examined. As a result of this examination, it was seen that EGA achieved better results in fewer iterations compared to classical GA.

Developing an Optimization Model for Minimizing Solid Waste Collection Costs

With the increase in population in cities, the number of solid waste to be collected has also increased. Because the garbage collection route must be traveled repeatedly, even minor improvements in these routes can result in a significant increase in fuel usage. Shortening the journey would provide a significant contribution to lowering fuel expenses in all towns, especially given the rising cost of fossil fuels. Furthermore, lowering fuel usage is critical for Turkey to meet its national objectives under the Paris Agreement. The Simulated Annealing (SA) algorithm, one of the heuristic optimization techniques used to identify the best solutions to complicated problems, is employed to solve the routing problem of solid waste collection vehicles in this study. This method, which was inspired by the metal annealing process, stands out for its ability to avoid regional minima while looking for the optimal solution. The applicant region was selected as the Kosova Neighborhood of Konya's Selçuklu District. The container distances needed for the method to execute were acquired by extracting the coordinates of the containers. Kosova Neighborhood was separated into 7 distinct regions due to the restricted capacity of rubbish collection vans. All regions were analyzed independently, and the best feasible routes were estimated using the SA algorithm approach, and the results were compared to the greedy algorithm findings. The SA algorithm outperformed the greedy algorithm by 26.49%.

Employing the Powered Hybridized Darts Game with BWO Optimization for Effective Job Scheduling and Distributing Load in the Cloud-Based Environment

One of the most frequent issues in cloud computing systems is job scheduling, which is designed to efficiently reduce installation time and cost while concurrently enhancing resource utilisation. Limitations such as accessible implementation costs, high resource utilisation, insufficient make-span, and fast scheduling response lead to the Nondeterministic Polynomial (NP)-hard optimisation problem. As the number of combinations along with processing power increases, job allocation becomes NP-hard. This study employs a hybrid heuristic optimisation technique that incorporates load balancing to achieve optimal job scheduling and boost service provider performance within the cloud architecture. As a result, there are many less problems with the scheduling process. The suggested work scheduling approach successfully resolves the load balancing issue. The suggested Hybridised Darts Game-Based Beluga Whale Optimisation Algorithm (HDG-BWOA) assists in assigning jobs to the machines according to workload. When assigning jobs to virtual machines, factors such as reduced energy usage, minimised mean reaction time, enhanced job assurance ratio, and higher Cloud Data Centre (CDC) resource consumption are taken into account. By ensuring flexibility among virtual computers, this job scheduling strategy keeps them from overloading or underloading. Additionally, by employing this method, more activities are effectively finished before the deadline. The effectiveness of the proposed configuration is guaranteed using traditional heuristic-based job scheduling techniques in compliance with multiple assessment metrics.

Hybridized Darts Game with Beluga Whale Optimization Strategy for Efficient Task Scheduling with Optimal Load Balancing in Cloud Computing

A cloud computing technology permits clients to use hardware and software technology virtually on a subscription basis. The task scheduling process is planned to effectively minimize implementation time and cost while simultaneously increasing resource utilization, and it is one of the most common problems in cloud computing systems. The Nondeterministic Polynomial (NP)-hard optimization problem occurs due to limitations like an insufficient make-span, excessive resource utilization, low implementation costs, and immediate response for scheduling. The task allocation is NP-hard because of the increase in the amount of combinations and computing resources. In this work, a hybrid heuristic optimization technique with load balancing is implemented for optimal task scheduling to increase the performance of service providers in the cloud infrastructure. Thus, the issues that occur in the scheduling process is greatly reduced. The load balancing problem is effectively solved with the help of the proposed task scheduling scheme. The allocation of tasks to the machines based on the workload is done with the help of the proposed Hybridized Darts Game-Based Beluga Whale Optimization Algorithm (HDG-BWOA). The objective functions like higher Cloud Data Center (CDC) resource consumption, increased task assurance ratio, minimized mean reaction time, and reduced energy utilization are considered while allocating the tasks to the virtual machines. This task scheduling approach ensures flexibility among virtual machines, preventing them from overloading or underloading. Also, using this technique, more tasks is efficiently completed within the deadline. The efficacy of the offered arrangement is ensured with the conventional heuristic-based task scheduling approaches in accordance with various evaluation measures.

Bare-bones multi-scale quantum harmonic oscillator algorithm for global optimization

The Multi-scale Quantum Harmonic Oscillator Algorithm (MQHOA) is a novel heuristic optimization technique, underpinned by the quantum wave function as its theoretical foundation. In this study, we present a streamlined MQHOA framework, dubbed the Bare-Bones Multi-scale Quantum Harmonic Oscillator Algorithm (BBMQHOA). This minimalist paradigm employs the most rudimentary free particle in quantum mechanics to simulate particle movement. BBMQHOA leverages group stability over individual stability, enabling the algorithm to fully capitalize on population data, thereby enhancing exploration and exploitation. An extensive array of experiments is carried out on a collection of 24 benchmark functions and a pair of real-world applications to validate the efficacy of our suggested methodology. The outcomes reveal that its performance is both practical and competitive while simultaneously maintaining user-friendliness.

Analysis of Heuristic Optimization Technique Solutions for Combined Heat-Power Economic Load Dispatch

Thermal power plants use coal as a fuel to create electricity while wasting a significant amount of energy as heat. If the heat and power plants are combined and used in cogeneration systems, it is possible to reuse the waste heat and hence enhance the overall efficiency of the power plant. In order to minimize production costs while taking system constraints into account, it is important to find out the optimal operating point of power and heat for each unit. Combined heat and power production is now widely used to improve thermal efficiency, lower environmental emissions, and reduce power generation costs. In order to determine the best solutions to the combined heat and power economic dispatch problem, several traditional as well as innovative heuristic optimization approaches were employed. This study offers a thorough analysis of the use of heuristic optimization techniques for the solution of the combined heat and power economic dispatch problem. In this proposed work, the most well-known heuristic optimization methods are examined and used for the solution of various generating unit systems, such as 4, 7, 11, 24, 48, 84, and 96, taking into account various constraints. This study analyzes how various evolutionary approaches are performed for various test systems. The heuristic methodologies’ best outcomes for various case studies with restrictions are contrasted.

A novel hybrid optimization model to determine optimum water resources for water supply of residential areas

In this study, a new hybrid model was improved by combining heuristic and numerical optimization algorithms to decide on optimum water resources based on their costs in the water supply. The purpose of the hybrid model is to reach a best result in the shortest time by simultaneously searching global and local minimums. Therefore, the steepest descent (SD) algorithm (numerical optimization method) was embedded in the classical modified clonal selection algorithm (the classical modified Clonalg) (one of artificial immune systems, heuristic optimization technique). This hybridization allows the SD algorithm to search local minimums while the classical modified Clonalg is searching a global minimum. The hybrid optimization model was applied to the cost objective function depending on distances and piezometric head differences between the water resources and destination. A scenario consists of five hypothetical water resources and one residential area/settlement. Herein, the aim is to satisfy the water demand of the residential area with a minimum cost from the water resources. The cost objective function was also minimized by the regular model (the classical model) according to the scenario, and their results were compared. Both models were run ten times for testing their stabilities. According to the results, the hybrid model is better than the regular model in terms of run time and stability. The hybrid model found a minimum cost for the water supply in a shorter time (in half) in comparison with the regular model in all runs.

Novel Heuristic Optimization Technique to Solve Economic Load Dispatch and Economic Emission Load Dispatch Problems

The fundamental objective of economic load dispatch is to operate the available generating units such that the needed load demand satisfies the lowest generation cost and also complies with the various constraints. With proper power system operation planning using optimized generation limits, it is possible to reduce the cost of power generation. To fulfill the needs of such objectives, proper planning and economic load dispatch can help to plan the operation of the electrical power system. To optimize the economic load dispatch problems, various classical and new evolutionary optimization approaches have been used in research articles. Classical optimization techniques are outdated due to many limitations and are also unable to provide a global solution to the ELD problem. This work uses a new variant of particle swarm optimization techniques called modified particle swarm optimization, which is effective and efficient at finding optimum solutions for single as well as multi-objective economic load dispatch problems. The proposed MPSO is used to solve single and multi-objective problems. This work considers constraints like power balance and power generation limits. The proposed techniques are tested for three different case studies of ELD and EELD problems. (1) The first case is tested using the data of 13 generating unit systems along with the valve point loading effect; (2) the second case is tested using 15 generating unit systems along with the ramp rate limits; and (3) the third case is tested using the economic emission dispatch (EELD) as a multi-objective problem for 6 generating unit systems. The outcomes of the suggested procedures are contrasted with those of alternative optimization methods. The results show that the suggested strategy is efficient and produces superior optimization outcomes than existing optimization techniques.

Reduction of Power Losses and Voltage Profile Improvement in a Smart Grid Incorporated with Electric Vehicles

Governments worldwide have adopted energy-saving policies out of concern for the planet. System efficiency and renewable energy are needed to reduce greenhouse gas emissions, which cause climate change. Electricity generation is the biggest polluter, followed by transportation. Electric vehicles would strain electricity infrastructure without technical solutions. This study uses a hybrid genetic algorithm particle sworn optimization (HGAPSO) to find the optimal switching and feeder reconfiguration approach. Meet transmission constraints while reducing real power losses and improving system bus voltage. In the context of power system change, what are the benefits of employing the HGAPSO approach as opposed to the GA method and the PSO method, respectively. HGAPSO increases network power losses and voltage dispersion. Improved algorithms can help solve this crucial issue. It uses many heuristic optimization techniques to reconfigure transmission network connectivity and determine the best configuration. Limit bus voltage changes while maintaining the system’s radial structure and lowering power usage. MATLAB’s IEEE 33-bus communication network evaluated procedure reliability and performance. The results show that the proposed method reduces power waste during standalone runs and speeds up processing.

A Snapshot-Stacked Ensemble and Optimization Approach for Vehicle Breakdown Prediction

Predicting breakdowns is becoming one of the main goals for vehicle manufacturers so as to better allocate resources, and to reduce costs and safety issues. At the core of the utilization of vehicle sensors is the fact that early detection of anomalies facilitates the prediction of potential breakdown issues, which, if otherwise undetected, could lead to breakdowns and warranty claims. However, the making of such predictions is too complex a challenge to solve using simple predictive models. The strength of heuristic optimization techniques in solving np-hard problems, and the recent success of ensemble approaches to various modeling problems, motivated us to investigate a hybrid optimization- and ensemble-based approach to tackle the complex task. In this study, we propose a snapshot-stacked ensemble deep neural network (SSED) approach to predict vehicle claims (in this study, we refer to a claim as being a breakdown or a fault) by considering vehicle operational life records. The approach includes three main modules: Data pre-processing, Dimensionality Reduction, and Ensemble Learning. The first module is developed to run a set of practices to integrate various sources of data, extract hidden information and segment the data into different time windows. In the second module, the most informative measurements to represent vehicle usage are selected through an adapted heuristic optimization approach. Finally, in the last module, the ensemble machine learning approach utilizes the selected measurements to map the vehicle usage to the breakdowns for the prediction. The proposed approach integrates, and uses, the following two sources of data, collected from thousands of heavy-duty trucks: Logged Vehicle Data (LVD) and Warranty Claim Data (WCD). The experimental results confirm the proposed system’s effectiveness in predicting vehicle breakdowns. By adapting the optimization and snapshot-stacked ensemble deep networks, we demonstrate how sensor data, in the form of vehicle usage history, contributes to claim predictions. The experimental evaluation of the system on other application domains also indicated the generality of the proposed approach.

A Cloud Computing-based Research on the Relationship between Educational Internship and Pre-Service English Teachers' Professional Development

Educational internships are a crucial component of teacher education programs, as they evaluated the experience practical opportunities for preschool teachers with the development of skills, experience of practical scenarios, and experience feedback. Through educational internships, pre-service teachers can also develop their professional identities and gain a deeper understanding of the complex challenges and rewards of teaching. This paper explores the relationship between educational internships and heuristic optimization cloud environments for the professional development of English pre-service teachers. The research presents a novel approach called Cloud Spider Wolf Optimization (CSWO) that utilizes cloud computing technology to enhance the effectiveness of educational internships. The study evaluates the impact of CSWO on pre-service teachers' professional development by examining their learning outcomes and perceptions of the internship program. The data for the analysis is collected through primary data among the pre-service English teachers. The data for analysis is collected from 200 pre-service teachers in academic schools in China. The results indicate that CSWO significantly improves pre-service teachers' professional development by providing them with opportunities to engage in authentic, real-world tasks that enhance their knowledge and skills in English language teaching. The study also suggests that the use of cloud computing technology can provide a valuable tool for enhancing the effectiveness of educational internships. The findings have important implications for teacher preparation programs and suggest that the integration of cloud computing technology and heuristic optimization techniques can be used to improve the quality of teacher education.

Solving the laminar boundary layer problem in heat transfer with heuristic optimization techniques

Heat transfer takes place in every aspect of our daily life. Many situations, such as energy conversion plants, heating devices, and cooling systems, focus on heat transfer. One of the subjects in heat transfer is the boundary layer of the laminar flow problem. Well-known exploratory algorithms are used to solve for the flow on a flat plate in this study. The algorithms used are genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), ant colony optimization for continuous domains (ACOR), artificial bee colony (ABC), and firefly algorithm (FA). The three properties, the layer thickness of the laminar boundary, heat flux, and the distance of the leading edge, are optimized. Each property is determined in three conditions; minimum, maximum, and target. The results showed that PSO, SA, ABC, and FA algorithms were more suitable than GA and ACOR algorithms. It has also been determined that the processing times are long in the FA and SA algorithms. The findings show that heuristic algorithms can find global results or results close to global results in heat transfer problems.

Optimization Technique for Renewable Energy Storage Systems for Power Quality Analysis with Connected Grid

Power quality is the main problem with the power system network. Poor electricity quality may cause disruptions and financial challenges for consumers. Additionally, it could cause electronic gadgets to overheat, be damaged, or operate inadvertently. Transformers and other components of the power distribution system may also overheat and experience core saturation. This study investigates potential problems with the quality of the electricity in a photovoltaic linked power system. This paper suggests a new optimization method for day-ahead trading and control in DC microgrid power management (MG). The goal of the multiobjective optimization dispatch (MOOD) problem is to lower overall operational costs as well as the costs associated with power loss in efficient conservation systems and exhaust emission quantities such as nitrogen oxides, sulphur dioxide, and carbon dioxide. Using the weighted sum approach, the multiobjective optimization problem is reduced to a single optimization problem. The analytical hierarchy process (AHP) method is then used to calculate the weight coefficients while accounting for each objective function’s preferences. Power balancing, high levels of renewable energy penetration, the most effective scheduling of battery charging and discharging, control of load curtailment, and the technical limitations of the system are all taken into account when evaluating the system’s performance in both grid-connected and standalone operation modes. The ant lion optimizer (ALO) technique is taken into account to tackle MOOD, comparing the effectiveness of the proposed method to other well-known heuristic optimization techniques. The simulation’s results demonstrate how effectively the proposed strategy can address the coordinated control and optimization dispatch difficulties. They also found that operating the MG system economically in grid-connected mode can save overall costs by roughly 4.70% compared to doing it in independent mode.

Improving structural health monitoring with impact zone localization using heuristic algorithms

AbstractThis paper presents a novel approach for reconstructing the characteristics of non-punctual impact events on elastic plates by introducing multi-parameter optimization. The objective function is minimized using two heuristic optimization techniques, particle swarm optimization, and differential evolution, to reconstruct impact force characteristics. The force was regarded as taking the form of a uniform pressure over a part of the plate called the patch. The Maxwell-Betti theorem was considered to decouple the problem of localization and the time history of the applying unknown load. The approach based on heuristic optimization methods has been proven a performance to locate the impact zone. A comparison between particle swarm optimization and differential evolution was discussed.

A genetic algorithm based heuristic optimization technique for solving balanced allocation problem involving overall shipping cost minimization with restriction to the number of serving units as well as customer hubs

In the context of balanced allocation of products from different warehouses to different customer hubs, a company may have to take decision on the maximum number of customer hubs to which products are to be supplied by each warehouse. Further decision on the number of warehouses, that should be associated with supplying products to each hub is also crucial. In this paper, a technique has been developed to facilitate the above-mentioned decision making related to balanced allocation problem (BAP) with a restriction to the maximum number of warehouses that can cater to the demand of each customer hub, and also with restriction to the maximum number of customer hubs that each warehouse can serve. The delivery cost of unit quantity of the product from each warehouse to each customer hub is fixed.The problem thus formulated has been solved with the help of Genetic algorithm (GA). The solution consists of the quantity distributed by each warehouse to each customer hub, the customer hubs chosen for allocation by each of the warehouses and the minimum total shipping cost. The model has been illustrated with a few numerical examples. Further, the effect on total optimum cost has been studied by altering the maximum number of warehouses that can cater to each customer hub, and also altering maximum number of customer hubs that each warehouse can serve. Finally, sensitivity analyses with respect to different GA parameters, as well as with respect to some model parameters have been done and displayed graphically.

Machine Learning-Based Modeling and Generic Design Optimization Methodology for Radio-Frequency Microelectromechanical Devices.

RF-MEMS technology has evolved significantly over the years, during which various attempts have been made to tailor such devices for extreme performance by leveraging novel designs and fabrication processes, as well as integrating unique materials; however, their design optimization aspect has remained less explored. In this work, we report a computationally efficient generic design optimization methodology for RF-MEMS passive devices based on multi-objective heuristic optimization techniques, which, to the best of our knowledge, stands out as the first approach offering applicability to different RF-MEMS passives, as opposed to being customized for a single, specific component. In order to comprehensively optimize the design, both electrical and mechanical aspects of RF-MEMS device design are modeled carefully, using coupled finite element analysis (FEA). The proposed approach first generates a dataset, efficiently spanning the entire design space, based on FEA models. By coupling this dataset with machine-learning-based regression tools, we then generate surrogate models describing the output behavior of an RF-MEMS device for a given set of input variables. Finally, the developed surrogate models are subjected to a genetic algorithm-based optimizer, in order to extract the optimized device parameters. The proposed approach is validated for two case studies including RF-MEMS inductors and electrostatic switches, in which the multiple design objectives are optimized simultaneously. Moreover, the degree of conflict among various design objectives of the selected devices is studied, and corresponding sets of optimal trade-offs (pareto fronts) are extracted successfully.

A review of inverse data envelopment analysis: origins, development and future directions

AbstractData envelopment analysis (DEA) is a widely used mathematical programming approach for assessing the efficiency of decision-making units (DMUs) in various sectors. Inverse DEA is a post-DEA sensitivity analysis approach developed initially for solving resource allocation. The main objective of inverse DEA is to determine the optimal quantity of inputs and/or outputs for each DMU under input and/or output perturbation (s), which would allow them to reach a given efficiency target. Since the early 2000s, inverse DEA has been extended theoretically and applied successfully in different areas including banking, energy, education, sustainability and supply chain management. In recent years, research has demonstrated the potential of inverse DEA for solving novel inverse problems, such as estimating merger gains, minimizing production pollution, optimizing business partnerships and more. This paper provides a comprehensive survey of the latest theoretical and practical advancements in inverse DEA while also highlighting potential areas for future research and development in this field. One such area is exploring the use of heuristic algorithms and optimization techniques in conjunction with inverse DEA models to address issues of infeasibility and nonlinearity. Moreover, applying inverse DEA to new sectors such as healthcare, agriculture and environmental and climate change issues holds great promise for future research. Overall, this paper sets the stage for further advancements in this promising approach.

Tek Modlu ve Çok Modlu Kıyaslama Fonksiyonlarını Kullanan Biyoloji Tabanlı Metasezgisel Optimizasyon Algoritmalarının Performans Karşılaştırması

Optimization is used in almost every aspect of our lives today and makes our lives easier. Optimization is generally studied as classical and heuristic optimization techniques. Classical optimization methods are not effective in real-world engineering problems. These methods, by their nature, require a mathematical model. Metaheuristic optimization methods have started to be used frequently today in the solution of these problems when a mathematical model cannot be created or a solution cannot be produced in an effective time even if it is created. These methods, by their nature, cannot produce effective results in all engineering problems. Therefore, new metaheuristic optimization methods are constantly being researched. In this study, quality test functions have been used to compare the performances of five algorithms that have been developed in recent years and produce effective results. The results obtained from these functions are shared in this study. It has been observed that the Artificial Hummingbird Optimization Algorithm (AHA) gives better results than other metaheuristic algorithms.