Multi-criteria Genetic Algorithm Research Articles

Mathematical Models for the Design of GRID Systems to Solve Resource-Intensive Problems

Artificial neural networks are successfully used to solve a wide variety of scientific and technical problems. The purpose of the study is to increase the efficiency of distributed solutions for problems involving structural-parametric synthesis of neural network models of complex systems based on GRID (geographically disperse computing resources) technology through the integrated application of the apparatus of evolutionary optimization and queuing theory. During the course of the research, the following was obtained: (i) New mathematical models for assessing the performance and reliability of GRID systems; (ii) A new multi-criteria optimization model for designing GRID systems to solve high-resource computing problems; and (iii) A new decision support system for the design of GRID systems using a multi-criteria genetic algorithm. Fonseca and Fleming’s genetic algorithm with a dynamic penalty function was used as a method for solving the stated multi-constrained optimization problem. The developed program system was used to solve the problem of choosing an effective structure of a centralized GRID system that was configured to solve the problem of structural-parametric synthesis of neural network models. To test the proposed approach, a Pareto-optimal configuration of the GRID system was built with the following characteristics: average performance–103.483 GFLOPS, cost–500 rubles per day, availability rate–99.92%, and minimum performance–51 GFLOPS.

ОПТИМИЗАЦИЯ ПРОЦЕССА УПРАВЛЕНИЯ РИСКАМИ В СФЕРЕ ПЕРЕВОЗКИ НЕФТЕПРОДУКТОВ АВТОМОБИЛЬНЫМ ТРАНСПОРТОМ

The article considers a model for optimizing the route of transportation of various dangerous goods, taking into account the equality of risks in an uncertain environment. The risks associated with traffic accidents are identified, as well as data on the number of accidents. A model is proposed, which is built in an uncertain environment, a multi-criteria genetic algorithm based on linear weighting is developed to solve the problem.

Thermoeconomic analysis and Multi-Objective optimization for the synthesis of green hydrogen and three different products via a novel energy system

• The simultaneous generation of the green hydrogen, methane, power, and drinking water were examined. • The offered plant was simulated in EES software based on exergoeconomic point of view. • Optimal operating point for the system was determined via multi-objective optimization. • The green hydrogen would be produced at a rate of 2.3 kg/h under well-balanced operating circumstances. • The exergy productivity was 12.76% and the total cost was 61.69 $/GJ at optimum conditions. This paper proposes an integrated energy conversion system relying on the parabolic trough solar collector field, a Rankine cycle, and a proton exchange membrane to generate green hydrogen and methane alongside power and freshwater. This study examines the power of effective variables on energy and economic factors before and after the integration of renewable energy sources. Simulating a system and obtaining the desired outcome is achieved by using an engineering equation solver. A genetic evolutionary algorithm code is used to find the optimal operating circumstance for the plant by applying a multi-criteria genetic evolutionary algorithm to the system. According to the results obtained from an energy standpoint, the exergy productivity is 12.76% and the total cost is 61.69 $/GJ at optimum operating conditions. Further optimization of the multi-objective optimization model shows that the optimum operating case, which is suitably-balanced among energy productivity and cost, has maximum energy productivity of 13.29% and a cost of 63.96 $/GJ, sequentially. Optimal working conditions are considered to correspond to exergy productivity of 10.01% and total cost of 60.21 $/GJ, sequentially. Further, hydrogen would be produced at a rate of 2.28 kg/h under well-balanced operating circumstances. A strong approach for optimizing the system operation and diminish total cost can be achieved by installing the condenser unit instead of the condenser.

Geometry optimisation of an industrial thermocline Thermal Energy Storage combining exergy, Life Cycle Assessment and Life Cycle Cost Analysis

Recently, packed-bed storage has been considered as a promising alternative solution for thermal energy storage especially for waste heat recovery in industrial plants. This work aims to optimise environmental footprint, costs and exergy efficiency of a thermocline thermal energy storage through two optimisation variables. These variables describe the tank shape and the particle grain size. Two solid filler materials are compared: machined ceramic and ceramic from fly ashes. The reference storage is an existing industrial high-temperature air/bauxite packed-bed storage called Eco-Stock®. A one-dimensional two-phase (fluid and solid) model is used to determine the energy and exergy performance of the thermocline tank. For the life cycle assessment, four indicators are selected: cumulative energy demand, global warming potential, abiotic depletion potential and particulate matter. Finally, a life cycle cost analysis is performed to determine levelised cost of energy used as economic criterion. This multi-objective problem is solved by the multi-criteria genetic algorithm available on the Matlab® platform. A Pareto set is obtained, bounded by the single exergy and environmental optimisation solutions. The economic optimisation is found on the Pareto set, close to the environmental optimal solution. Favouring economic performance reduces the environmental footprint of the storage. Despite better exergy performance and smaller tank volume, the exergy-optimised tank increases environmental impacts and costs due to higher pumping work. The environmental and economic optimisations lead to stocky tank shapes while a tapered tank is obtained for the exergy optimisation. According to the TOPSIS method, the economic optimal solution appears to be the best trade-off for both fillers tested. Despite poorer thermophysical properties, the solution with ceramic from fly ashes shows similar exergy and economic performance as the machined ceramic solution (more than 96 % of exergy efficiency for 3.1 c€/kWhth), while the environmental footprint is greatly reduced (61 vs 87 ca.year). This tank has a diameter of 2.6 m and a height of 1.7 m. The particle diameter is 11 mm.

Formation of Fuzzy Patterns in Logical Analysis of Data Using a Multi-Criteria Genetic Algorithm

The formation of patterns is one of the main stages in logical data analysis. Fuzzy approaches to pattern generation in logical analysis of data allow the pattern to cover not only objects of the target class, but also a certain proportion of objects of the opposite class. In this case, pattern search is an optimization problem with the maximum coverage of the target class as an objective function, and some allowed coverage of the opposite class as a constraint. We propose a more flexible and symmetric optimization model which does not impose a strict restriction on the pattern coverage of the opposite class observations. Instead, our model converts such a restriction (purity restriction) into an additional criterion. Both, coverage of the target class and the opposite class are two objective functions of the optimization problem. The search for a balance of these criteria is the essence of the proposed optimization method. We propose a modified evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to solve this problem. The new algorithm uses pattern formation as an approximation of the Pareto set and considers the solution’s representation in logical analysis of data and the informativeness of patterns. We have tested our approach on two applied medical problems of classification under conditions of sample asymmetry: one class significantly dominated the other. The classification results were comparable and, in some cases, better than the results of commonly used machine learning algorithms in terms of accuracy, without losing the interpretability.

A Comprehensive Thermoeconomic Evaluation and Multi-Criteria Optimization of a Combined MCFC/TEG System

In this study, an integrated molten carbonate fuel cell (MCFC), thermoelectric generator (TEG), and regenerator energy system has been introduced and evaluated. MCFC generates power and heating load. The exit fuel gases of the MCFC is separated into three sections: the first section is transferred to the TEG to generate more electricity, the next chunk is conducted to a regenerator to boost the productivity of the suggested plant and compensate for the regenerative destructions, and the last section enters the surrounding. Computational simulation and thermodynamic evaluation of the hybrid plant are carried out utilizing MATLAB and HYSYS software, respectively. Furthermore, a thermoeconomic analysis is performed to estimate the total cost of the product and the system cost rate. The offered system is also optimized using multi-criteria genetic algorithm optimization to enhance the exergetic efficiency while reducing the total cost of the product. The power generated by MCFC and TEG is 1247.3 W and 8.37 W, respectively. The result explicates that the provided electricity and provided efficiency of the suggested plant is 1255.67 W and 38%, respectively. Exergy inquiry outcomes betokened that, exergy destruction of the MCFC and TEG is 13,945.9 kW and 262.75 kW, respectively. Furthermore, their exergy efficiency is 68.22% and 97.31%, respectively. The impacts of other parameters like working temperature and pressure, thermal conductance, the configuration of the advantage of the materials, etc., on the thermal and exergetic performance of the suggested system are also evaluated. The optimization outcomes reveal that in the final optimum solution point, the exergetic efficiency and total cost of the product s determined at 70% and 30 USD/GJ.

Multicriterial analyses of Pareto-efficiency for collaborative multi-agent systems using genetic algorithm with variations

The paper is devoted to an emerging trend in modern automation of production - collaborative multi-agent systems – characterized by the simultaneous use of the labor of people and robots in a single system in the framework of solving the target problem. The need in collaborative multi-agent systems is due to the fact that the industrial use of fully automated multicomponent systems is limited by the financial and infrastructural unavailability of various industries to switch to completely unmanned technologies. The article discusses the issue of the effectiveness of applying this approach as an alternative to full robotization of production. A multi-criteria assessment of the effectiveness of collaborative multi-agent systems as optimization knapsack problem is formulated. A multicriteria genetic algorithm based on the principle of small variations of the basic solution is presented for the solution of the stated problem. The resulting Pareto set is constructed according to the rank of possible solutions.

OPTIMAL GENETIC ALGORITHM SELECTION FOR DEEP NEURAL NETWORK SETTINGS

The problem of construction of deep neural networks with the use of genetic algorithms is considered. The problem of structural-parametric synthesis with creation of neural networks is defined. The main purpose of the study is to find a deep neural network that is optimal for solving urgent problems. The classification problem is chosen as the urgent problem to solve. Also the classification of genetic algorithms is given, which are used as a basis for establishing the parameters of deep neural networks A system for the optimal tuning of the parameters of deep neural networks is proposed, which includes a two-stage algorithm. At the first stage of the algorithm, a multicriteria genetic algorithm is selected from a set of possible ones (genetic algorithm of vector estimation, genetic algorithm of Fonseca and Fleming, genetic algorithm of Pareto approximation with niche, genetic algorithm of sorting without dominance, genetic algorithm of Pareto force, genetic algorithm of Pareto-2 force ) that best fits the given training sample. At the second step, the problem of structural-parametric synthesis of a neural network is solved according to the criteria of accuracy and complexity. As a result of training, the values of the neural network parameters are found, such as: the number of layers, the number of neurons in each layer, the values of the weight coefficients.The modeling of the proposed system is carried out. The results of modeling, comparison of results with similar software packages are presented. The obtained results show the possibilities of wide use.

Multivariate Optimization in Large-Scale Building Problems: An Architectural and Urban Design Approach for Balancing Social, Environmental, and Economic Sustainability

This paper explores the promise of genetic algorithms as a tool for optimization of buildings at a neighborhood scale across the conflicting demands of social, environmental, and economic sustainability. A large urban site in Chicago, Illinois, is selected to test the viability of using a multi criteria genetic algorithm to optimize the potential building mix in a newly planned development. Two variables, the number of buildings of a given use-type and their height, are analyzed against cost functions for social, economic, and environmental objectives. Single-objective algorithms are utilized to optimize the variables individually. A non-dominated genetic sorting algorithm (NGSAII) is then utilized to identify the Pareto-optimal solutions considering the three objectives simultaneously. Single-objective results are found to vary substantially by objective, with different variable values for social, economic, and environmental sustainability. For multi-objective algorithms, the results support Campbell’s notion of the three nodes of sustainability being in conflict. Solutions performing well across economic and environmental objectives were most common. Solutions performing well among environmental and social objectives were less common. Solutions performing well across economic and social performance were rare. This suggests that while economic and environmental conflicts are to some degree resolvable, conflicts between social and either of economic or environmental performance are more difficult to resolve. The failure of any solution to perform well across all three objectives lends credence to the idea of design as a series of trade-offs and that one super optimum solution may not exist. The process provides insights into the trade-offs implicit in the building design and development process and raises questions regarding the balancing of competing sustainability objectives.

Build orientation optimization for lightweight lattice parts production in selective laser melting by using a multicriteria genetic algorithm

Abstract

Service-oriented Application Composition with Evolutionary Heuristics and Multiple Criteria

The need to create and deploy business application systems rapidly has sparked interest in using web services to compose them. When creating mission-critical business applications through web service compositions, in addition to ensuring that functional requirements are met, designers need to consider the end-to-end reliability, security, performance, and overall cost of the application. As the number of available coarse-grain business services grows, the problem of selecting appropriate services quickly becomes combinatorially explosive for realistic-sized business applications. This article develops a business-process-driven approach for composing service-oriented applications. We use a combination of weights to explore the entire QoS criteria landscape through the use of a multi-criteria genetic algorithm (GA) to identify a Pareto-optimal multidimensional frontier that permits managers to trade off conflicting objectives when selecting a set of services. We illustrate the effectiveness of the approach by applying it to a real-world drop-ship business application and compare its performance to another GA-based approach for service composition.

Models Of Uncertain-Random Programming

The article proposes the models of optimization with constraints under conditions of parametric mixed uncertainty ‒ aleatory and epistemic. We model parameters with aleatory uncertainty by random values with probability distribution functions obtained from statistical data. We model parameters with epistemic uncertainty by uncertain values introduced in the uncertainty theory of Liu B. Experts define the uncertainty distribution functions. We model a function of random and uncertain parameters by uncertain-random value, interpreted as epistemic value parameterized by random values. Optimization criteria (deterministic duplicates of objective functions) are combination of different characteristics of random and uncertain values, which allows both to average objective functions and to take into account risks or reliability arising from the variability of random and uncertain values. Using the proposed models of uncertain-random programming, we formalized as a two-criterion optimization problem with constraints and solved the task of preliminary aerodynamic design in the conditions of parametric mixed uncertainty ‒ calculation of aircraft weight parameters. The uncertainty theory makes possible under certain conditions (for sufficiently wide class of functions) to obtain analytical expressions for characteristics of uncertain functions, that significantly reduces computational costs. To calculate weight parameters of aircraft, we use multicriteria genetic algorithm and statistical modeling. We investigate the dependence of the optimization result on the given probability levels for random values and the expert belief degree for epistemic values reflecting the reliability of the obtained solution. As result of applying the proposed models for calculating the weight parameters of the aircraft, we obtained the Pareto fronts shown in the figures.

Implementation of generic algorithm in map-matching model

This paper proposed a novelty map-matching method based on multi-criteria genetic algorithm. In addition, the new ideas are introduced, such as incorporation of dynamic time wrapping method and dynamic programming technique. Dynamic time wrapping is used to evaluate the geometric similarity between recorded trajectory and observed route, while dynamic programming is used to accelerate the calculations of fitness functions in genetic algorithm. Model is tested on real data collected from the street network, which yielded good results with regard to accuracy and running time. Furthermore, it is shown that proposed model is capable to process the GPS data, recorded on dense street network with negligible amount of errors, which is the most challenging task for majority of map-matching algorithms. Therefore, the main impact of this research is observed through the development of better and more precise global map-matching algorithm. This is really important for expert and intelligent transport systems, which are based on input data collected by floating car data systems. This type of system relies on high quality post-processing map-matching model, which should provide accurate results in order to ensure that intelligent transport system makes more precise estimations and predictions of traffic conditions.

Multi-criteria reliability optimization for a complex system with a bridge structure in a fuzzy environment: A fuzzy multi-criteria genetic algorithm approach

Multi-criteria reliability optimization for a complex system with a bridge structure in a fuzzy environment: A fuzzy multi-criteria genetic algorithm approach

Test Case Prioritization Using Metaheuristic Search Techniques

In this paper, Artificial Particle Swarm Optimization (PSO) inspired by real Swarm social–psychological tendency is used to solve time constraint prioritization problem-the techniques to prioritize the test cases that finds faults as early as possible, or maximize the rate of fault detection in the suite. The proposed technique is compared with three searches based metaheuristic approaches–(1) an ant-colony optimization approach, (2) Cuscuta search algorithm and (3) Hybrid Particle Swarm Optimization algorithm and two evolutionary metaheuristic- (1) Multi-Criteria Genetic algorithm technique which the fitness is APFD and (2) Multi-Criteria Genetic algorithm technique which the fitness is the proposed fitness multiplied by APFD and with five other non-search based prioritization techniques- (1) optimal, (2) random, (3) reverse, (4) untreated and (5) average faults found per minute algorithm based ordering. We investigate whether the proposed PSO metaheuristic outperforms existing prioritizing techniques in terms of APFD Score.

Network traffic control based on a mesoscopic dynamic flow model

The paper focuses on Network Traffic Control based on aggregate traffic flow variables, aiming at signal settings which are consistent with within-day traffic flow dynamics. The proposed optimisation strategy is based on two successive steps: the first step refers to each single junction optimisation (green timings), the second to network coordination (offsets). Both of the optimisation problems are solved through meta-heuristic algorithms: the optimisation of green timings is carried out through a multi-criteria Genetic Algorithm whereas offset optimisation is achieved with the mono-criterion Hill Climbing algorithm. To guarantee proper queuing and spillback simulation, an advanced mesoscopic traffic flow model is embedded within the network optimisation method. The adopted mesoscopic traffic flow model also includes link horizontal queue modelling. The results attained through the proposed optimisation framework are compared with those obtained through benchmark tools.

Multi-criteria genetic algorithm applied to scheduling in multi-cluster environments

Scheduling and resource allocation to optimize performance criteria in multi-cluster heterogeneous environments is known as an NP-hard problem, not only for the resource heterogeneity, but also for the possibility of applying co-allocation to take advantage of idle resources across clusters. A common practice is to use basic heuristics to attempt to optimize some performance criteria by treating the jobs in the waiting queue individually. More recent works proposed new optimization strategies based on Linear Programming techniques dealing with the scheduling of multiple jobs simultaneously. However, the time cost of these techniques makes them impractical for large-scale environments. Population-based meta-heuristics have proved their effectiveness for finding the optimal schedules in large-scale distributed environments with high resource diversification and large numbers of jobs in the batches. The algorithm proposed in the present work packages the jobs in the batch to obtain better optimization opportunities. It includes a multi-objective function to optimize not only the Makespan of the batches but also the Flowtime, thus ensuring a certain level of QoS from the users’ point of view. The algorithm also incorporates heterogeneity and bandwidth awareness issues, and is useful for scheduling jobs in large-scale heterogeneous environments. The proposed meta-heuristic was evaluated with a real workload trace. The results show the effectiveness of the proposed method, providing solutions that improve the performance with respect to other well-known techniques in the literature.

Applying a multi-criteria genetic algorithm framework for brownfield reuse optimization: Improving redevelopment options based on stakeholder preferences

The reuse of underused or abandoned contaminated land, so-called brownfields, is increasingly seen as an important means for reducing the consumption of land and natural resources. Many existing decision support systems are not appropriate because they focus mainly on economic aspects, while neglecting sustainability issues. To fill this gap, we present a framework for spatially explicit, integrated planning and assessment of brownfield redevelopment options. A multi-criteria genetic algorithm allows us to determine optimal land use configurations with respect to assessment criteria and given constraints on the composition of land use classes, according to, e.g., stakeholder preferences. Assessment criteria include sustainability indicators as well as economic aspects, including remediation costs and land value. The framework is applied to a case study of a former military site near Potsdam, Germany. Emphasis is placed on the trade-off between possibly conflicting objectives (e.g., economic goals versus the need for sustainable development in the regional context of the brownfield site), which may represent different perspectives of involved stakeholders. The economic analysis reveals the trade-off between the increase in land value due to reuse and the costs for remediation required to make reuse possible. We identify various reuse options, which perform similarly well although they exhibit different land use patterns. High-cost high-value options dominated by residential land use and low-cost low-value options with less sensitive land use types may perform equally well economically. The results of the integrated analysis show that the quantitative integration of sustainability may change optimal land use patterns considerably.

Multi criteria genetic algorithm for optimal blending of coal

The present paper deals with the optimal planning for blending of coal of different grades for beneficiation of coal with a view to satisfy the requirements of the end users with desired specifications. The input specifications are known whereas aspiration levels of the characteristics washed coal have been specified. Beneficiation of coal refers to the production of wash coal from raw coal with the help of some suitable beneficiation/coal washing technologies. The processed coal is used by the different steel plants to serve their purpose during the manufacturing process of steel. The aim is to fix the level of the raw coal samples from different coal seams to be used/fed for the beneficiation to meet the desired target of the coal blending indicators, yield to maximum extent and to restrict the input cost of raw coal to be fed for beneficiation. The problem is considered as multi criteria decision making problem and solved using multi objective genetic algorithm. A case study from a regional coal company situated at Jharia coalfield, India has been made and solved using the proposed model.

A genetic algorithm for the design of job rotation schedules considering ergonomic and competence criteria

Job rotation is an organizational strategy increasingly used in manufacturing systems as it provides benefits to both workers and management in an organization. Job rotation prevents musculoskeletal disorders, eliminates boredom and increases job satisfaction and morale. As a result, the company gains a skilled and motivated workforce, which leads to increases in productivity, employee loyalty and decreases in employee turnover. A multi-criteria genetic algorithm is employed to generate job rotation schedules, with considering the most adequate employee-job assignments to prevent musculoskeletal disorders caused by accumulation of fatigue. The algorithm provides the best adequacy available between workers and the competences needed for performing the tasks. The design of the rotation schedules is based not only on ergonomic criteria but also on issues related to product quality and employee satisfaction. The model includes the workers’ competences as a measure for the goodness of solutions.