Evolutionary Algorithm-based Optimization Research Articles

Modeling Gas Hydrate Dynamics: Experimental Validation and Evolutionary Algorithm-Based Optimization

Modeling Gas Hydrate Dynamics: Experimental Validation and Evolutionary Algorithm-Based Optimization

Ontology-Based Driving Simulation for Traffic Lights Optimization

Traffic lights optimization is one of the principal components to lessen the traffic flow and travel time in an urban area. The present article seeks to introduce a novel procedure to design the traffic lights in a city using evolutionary-based optimization algorithms in combination with an ontology-based driving behavior simulation framework. Accordingly, an ontology-based knowledge base is introduced to provide a machine-understandable knowledge of roads and intersections, traffic rules, and driving behaviors. Then, a simulation environment is developed to inspect car behavior in real time. To optimize the traffic lights, a sine-based equation was defined for each traffic light, and the total travel time of the vehicles was considered as the cost function in the optimization algorithm. The optimization was performed with 5, 10, 15, 20, 25, and 30 vehicles in the urban areas. Based on the results, in contrast to uncontrolled intersections without traffic lights, optimized traffic lights can significantly contribute to total travel time-saving. To conclude, due to an escalation in the number of vehicles, the significance of optimized traffic lights has encountered an increase, and unoptimized traffic lights could increase total travel time even more than a city deprived of any traffic light.

A parallelized hybrid genetic algorithm with differential evolution for heat exchanger network retrofit

The challenge of heat exchanger network retrofit is often addressed using deterministic algorithms. However, the complexity of the retrofit problems, combined with multi-period operation, makes it very difficult to find any feasible solution. In contrast, stochastic algorithms are more likely to find feasible solutions in complex solution spaces. This work presents a customized evolutionary based optimization algorithm to address this challenge. The algorithm has two levels, whereby, a genetic algorithm optimizes the topology of the heat exchanger network on the top level. Based on the resulting topology, a differential evolution algorithm optimizes the heat loads of the heat exchangers in each operating period. The following bullet points highlight the customization of the algorithm:•The advantage of using both algorithms: the genetic algorithm is used for the topology optimization (discrete variables) and the differential evolution for the heat load optimization (continuous variables).•Penalizing and preserving strategies are used for constraint handling•The evaluation of the genetic algorithm is parallelized, meaning the differential evolution algorithm is performed on each chromosome parallel on multiple cores.

Soft computing based optimization of a novel solar heliostat integrated energy system using artificial neural networks

This study proposes and investigates a novel energy system based on biomass and solar energy. This plant is composed of a biomass unit, a solar unit, and a waste-heat recovery unit. This novel proposed integrated system can provide the needs such as electricity, hydrogen, freshwater, heating, and hot water production. For electricity generation, two gas turbines, one steam Rankine cycle, and one organic Rankine cycle are used. In contrast, for utilization of solar energy, a heliostat field, and for biomass conversion, a gasifier is used. In addition, the desalination unit and PEM electrolyzer are utilized to produce fresh water and hydrogen, respectively. Firstly, the present work aims to investigate the developed system from the exergoeconomic and environmental perspective. Multi-objective optimization is conducted to determine the maximum amount of exergetic efficiency and the minimum value of the cost rate. An artificial neural network (ANN) is employed as a mediator tool to accelerate the optimization process. The relation between objective functions and design parameters is studied utilizing ANN to obtain the plant optimal decision variables. Employing the Pareto Envelope-based selection algorithm II (PESA-II) method, the optimum amount for the total cost rate and exergy efficiency is found 224.1 $/h and 26.7%, respectively. In addition, three evolutionary-based optimization algorithms are applied to determine the optimum results of the suggested plant.

A clustering approach for EOS lumping — Using evolutionary-based metaheuristic optimization algorithms

It is always desirable to keep the number of components in compositional simulation low due to the high CPU time and storage space requirement; on the other hand, the model's accuracy in describing the phase behavior decreases with reducing the number of components. As a result, an optimal lumping approach is needed to group the components to attain high accuracy in the least amount of time. This paper's main objective is to find the best lumping scheme to maintain the split model's accuracy and reach an optimal lumped model in the least amount of time. In accordance with this purpose, five different evolutionary-based metaheuristic optimization algorithms known as genetic algorithm (GA), differential evolution (DE), harmony search (HS), imperialist competitive algorithm (ICA), and shuffled-frog leaping algorithm (SFLA) are incorporated into the objective function developed to cluster heavy fractions using the k-means algorithm. To find the optimal algorithm, the best cost of the objective function and the number of function evaluations (NFE) have been compared, and the final results were substantiated using flash calculation outputs, such as density, viscosity, phase diagram, and Wilson plot. The results indicate that the proposed algorithms are capable of finding the optimal lumped model with high accuracy for all reservoir samples tested in this study. This study also marked ICA as the best evolutionary-based optimization algorithm that can be used in clustering problems.

An evolutionary algorithm-based optimization method for the classification and quantification of steatosis prevalence in liver biopsy images

Non-alcoholic fatty liver disease (NAFLD) covers a range of chronic medical conditions varying from hepatocellular inflammation which characterizes nonalcoholic steatohepatitis (NASH) to steatosis, as the key element of a nonalcoholic fatty liver (NAFL). It is globally linked to the increasing prevalence of obesity and other components of metabolic syndrome and is expected to be the main indication for the existence of the liver disease in the coming years. Its eradication has become a major challenge due to the difficulties in clinical diagnosis, complex pathogenesis and the lack of approved therapies. In this study, an automated image analysis method is presented providing quantitative assessments of fat deposition in steatotic liver biopsy specimens. The methodology applies image processing, machine learning and evolutionary algorithm optimization techniques, producing a 1.93% mean classification error compared to the semiquantitative interpretations of specialized hepatologists. • An automated diagnostic tool for the accurate steatosis prevalence quantification in NAFLD biopsy images. • Image processing analysis for determining the liver tissue area and detecting circular objects of interest. • Machine Learning versus Deep Learning in Biopsy Image Analysis.

A precedence-rule-based heuristic for satellite onboard activity planning

Satellite onboard activity planning refers to the process of arranging a set of onboard activities on a timeline. It is typically formulated as a complex optimization problem with various constraints that is similar to the classic flexible job-shop scheduling problem. This paper first formulates a practical mathematical model considering both the constraints of onboard resources and the sequential logic among the activities. Then an efficient heuristic with a set of knowledge-guided precedence rules for satellite onboard activity planning is proposed, which can achieve real-time planning of the onboard mission. Simulation results indicate that the proposed heuristic can very efficiently produce a satisfied planning scheme, which significantly outperforms the evolutionary algorithm-based optimization method in practical engineering applications.

Design and bio-inspired optimization of direct contact membrane distillation for desalination based on constructal law

At the present study, a one-dimensional model for the flat sheet direct contact membrane distillation (DCMD) for desalination purposes is proposed. Flows and membrane properties have been estimated by appropriate temperature-dependent correlations. Results show that the numerical model is in a very good agreement with experimental data at various feed temperatures, flow rates and concentrations. A constructal design is investigated for DCMD to assess how constructal law can improve the DCMD performance. With the same thermal efficiency of 93.5%, constructal design improves the water mass flux by 37.5% in comparison with the conventional DCMD design. Also, an evolutionary-based optimization algorithm is employed to increase the efficiency of the constructal and conventional design. The Pareto frontier of the constructal and conventional design is compared with each other and the correlations between design variables are investigated. Overall, the present study demonstrates how constructal law can increase the performance of energy systems with a simple modification.

Comprehensive Study on Metaheuristics FADE Based Artificial Bee Colony Optimization Algorithm to Improve Performance of Wireless Networks

Over the last few years, it is been experienced wireless sensor networks technologies are grabbing huge attention in almost every aspect of human lives due to its vast coverage in real-life applications. It has emerged as one of the important and very promising technologies with lots of potential from every section due to its importance in wireless information transmission. WSNs due to their useful characteristics are being considered vulnerable to several possible security attacks which may affect the performance of the system. Among these issues, most challenging issues such as sinkhole which is considered as the most dangerous attack in WSN to reduce network performance. It prevents the base station in the process of gathering complete and unmodified data from its source. This work inspired by the integration of FADE and ABC presents a new variant of the metaheuristics fuzzy adaptive differential evolution based optimization algorithm to improve the performance of a wireless sensor network.

Structure evolution-based design for low-pass IIR digital filters with the sharp transition band and the linear phase passband

Digital filters are generally designed by identifying the transfer functions. Most researches are focused on the goal of approaching the desired frequency response and take less additional consideration of structure characteristics. As a matter of fact, structure characteristics can greatly affect the performance of the digital filter. If only the frequency response is considered, the identified transfer function may not be the optimum. In this situation, structure synthesis is also limited by the form of the identified transfer function. This paper proposes a structure evolution-based optimization algorithm which allows the integrated consideration of structure issues and frequency response specifications in design stage. The method generates digital filter structures by a structurally automatic generation algorithm which can randomly generate and effectively represent digital structures. The structures, seen as chromosomes, are evolved over genetic algorithm for the search of the optimal solution in structure space. They are evaluated according to the mean square error between the designed and the desired frequency responses. Simulation results validate that the algorithm designs diversified structures of digital filters, and they meet target frequency specifications and structure constraints tightly. It is a promising way for optimized and automated design of digital filters.

Representations of Search Spaces in the Problem of Mutational Pressure Optimization According to Protein-Coding Sequences.

The proper representation of the search space is the fundamental step in every optimization task, because it has a decisive impact on the quality of potential solutions. In particular, this problem appears when the search spaces are nonstandard and complex, with the large number of candidate solutions that differ from classical forms usually investigated. One of such spaces is the set of continuous-time, homogenous, and stationary Markov processes. They are commonly used to describe biological phenomena, for example, mutations in DNA sequences and their evolution. Because of the complexity of these processes, the representation of their search space is not an easy task but it is important for effective solving of the biological problems. One of them is optimality of mutational pressure acting on protein-coding sequences. Therefore, we described three representations of the search spaces and proposed several specific evolutionary operators that are used in evolutionary-based optimization algorithms to solve the biological problem of mutational pressure optimality. In addition, we gave a general formula for the fitness function, which can be used to measure the quality of potential solutions. The structures of these solutions are based on two models of DNA evolution described by substitution-rate matrices, which are commonly used in phylogenetic analyzes. The proposed representations have been successfully utilized in various issues, and the obtained results are very interesting from a biological point of view. For example, they show that mutational pressures are, to some extent, optimized to minimize cost of amino acid substitutions in proteins.

Structure Evolution Based Optimization Algorithm for Low Pass IIR Digital Filter Design

Digital filters are generally designed by identifying the transfer functions. Most researches are focused on the goal of approaching the desired frequency response, and take less additional consideration of structure characteristics which can greatly affect the performance of the digital filter. This paper proposes a structure-evolution based optimization algorithm (SEOA) which allows the integrated consideration of structure issues and frequency response specifications in design stage. The method generates digital filter structures by a structurally automatic-generation algorithm (SAGA) which can randomly generate and effectively represent digital structures. The structures, seen as chromosomes, are evolved over genetic algorithm (GA) for the search of the optimal solution in structure space. They are evaluated according to the mean squared error (MSE) between the designed and the desired frequency responses. Simulation results validate that the algorithm designs diversified structures of digital filters and they meet target frequency specifications and structure constraints tightly. It is a promising way for optimized and automated design of digital filters.

An evolutionary-based optimization algorithm for truss sizing design

In this paper, the optimal sizing of truss structures is solved using a novel evolutionary-based optimization algorithm. The efficiency of the proposed method lies in the combination of global search and local search, in which the global move is applied for a set of random solutions whereas the local move is performed on the other solutions in the search population. Three truss sizing benchmark problems with discrete variables are used to examine the performance of the proposed algorithm. Objective functions of the optimization problems are minimum weights of the whole truss structures and constraints are stress in members and displacement at nodes. Here, the constraints and objective function are treated separately so that both function and constraint evaluations can be saved. The results show that the new algorithm can find optimal solution effectively and it is competitive with some recent metaheuristic algorithms in terms of number of structural analyses required.

Rooftop air-conditioning unit performance improvement using refrigerant circuitry optimization

This study demonstrates the performance improvement of an air-to-air 7.5 Ton (26.4 kW) rooftop unit (RTU) achieved by optimizing an evaporator's refrigerant circuitry using evolutionary algorithms. We measured its cooling capacity and Coefficient of Performance (COP) in an environmental chamber where we also measured the in-situ air velocity profile using Particle Image Velocimetry (PIV).We used a detailed heat exchanger model to simulate the performance of the original evaporator operating with the measured air distribution, and then we used an evolutionary algorithm-based optimization module embedded in the simulation tool to optimize the refrigerant circuitry for the measured inlet air distribution.The optimized refrigerant circuitry design was implemented in a new prototype evaporator, which replaced the original evaporator in the tested RTU system. Laboratory tests of the new system showed an improvement of 2.2 ± 1.5% in capacity and 2.9 ± 1.5% in COP compared to the performance of the original system, which was optimized by the manufacturer using conventional methods prior to the onset of this study.

A systematic approach to determining metamodel scope for risk-based optimization and its application to water distribution system design

Metamodels have proven be very useful when it comes to reducing the computational requirements of Evolutionary Algorithm-based optimization by acting as quick-solving surrogates for slow-solving fitness functions. The relationship between metamodel scope and objective function varies between applications, that is, in some cases the metamodel acts as a surrogate for the whole fitness function, whereas in other cases it replaces only a component of the fitness function. This paper presents a formalized qualitative process to evaluate a fitness function to determine the most suitable metamodel scope so as to increase the likelihood of calibrating a high-fidelity metamodel and hence obtain good optimization results in a reasonable amount of time. The process is applied to the risk-based optimization of water distribution systems; a very computationally-intensive problem for real-world systems. The process is validated with a simple case study (modified New York Tunnels) and the power of metamodelling is demonstrated on a real-world case study (Pacific City) with a computational speed-up of several orders of magnitude.

Strategies to Develop Warm Solutions for Real-Time Pump Scheduling for Water Distribution Systems

An optimal pump operation schedule that maintains satisfactory hydraulics conditions can generally reduce energy consumptions compared to the traditional trial and error based pump operation schedule. Linking an evolutionary based optimization algorithm with a hydraulic simulation model has gained attention for obtaining the optimal schedule. However, this technique requires significant computation time and thus has difficulty in real-time implementation. This paper presents several tactics to generate warm solutions that can be used in the initial population of the evolutionary algorithms to reduce the computation times. Strategies to generate warm solutions include the use of linear programming, surrogate model known as machine learning or meta-model, and historical pump schedule for similar demand pattern. Providing warm solutions from approximate methods or previous day’s results to stochastic search methods can improve solution convergence and offers significant computation time benefits. Results obtained from different strategies are compared.

Distributed Robust Synchronization of Dynamical Networks With Stochastic Coupling

This paper deals with the problem of robust adaptive synchronization of dynamical networks with stochastic coupling by means of evolutionary algorithms. The complex networks under consideration are subject to: 1) the coupling term in a stochastic way is considered; 2) uncertainties exist in the node's dynamics; and 3) pinning distributed synchronization is also considered. By resorting to Lyapunov function methods and stochastic analysis techniques, the tasks to get the distributed robust synchronization and distributed robust pinning synchronization of dynamical networks are solved in terms of a set of inequalities, respectively. The impacts of degree information, stochastic coupling, and uncertainties on synchronization performance, i.e., mean control gain and convergence rate, are derived theoretically. The potential conservativeness for the distributed robust pinning synchronization problem is solved by means of an evolutionary algorithm-based optimization method, which includes a constraint optimization evolutionary algorithm and a convex optimization method and aims at improving the traditional optimization methods. Simulations are provided to illustrate the effectiveness and applicability of the obtained results.

A Differential Evolution Based Optimization Algorithm for Production Planning Model in Defense Industry

The production planning problem in defense industry has the non-regular nonlinearity of the objective function and the NP-hard nature of the solution space. A nonlinear integer programming model was proposed for this problem. A differential evolution based optimization algorithm was developed to solve the proposed model. A case study validated the effectiveness of the developed algorithm, and proved that it is superior to the standard genetic algorithm on the global searching capability. The algorithm can support the weapons production planning, and also can be applied to other industries.

Fixed-size ensemble classifier system evolutionarily adapted to a recurring context with an unlimited pool of classifiers

This paper presents a novel ensemble classifier system designed to process data streams featuring occasional changes in their characteristics (concept drift). The ensemble is especially effective when the concepts reappear (recurring context). The system collects information on emerging contexts in a pool of elementary classifiers trained on subsequent data chunks. The pool is updated only when concept drift is detected. In contrast to other ensemble solutions, classifiers are not removed from the pool, and therefore, knowledge of past contexts is preserved for future use. To ensure high classification performance, the number of classifiers contributing to decision-making is fixed and limited. Only selected elements from the pool can join the decision-making ensemble. The process of selecting classifiers and adjusting their weights is realized by an evolutionary-based optimization algorithm that aims to minimize the system misclassification rate. Performance of the system is evaluated through a series of experiments presenting some key features of the system.

AN ANT COLONY SYSTEM BASED DECISION SUPPORT SYSTEM FOR CONSTRUCTION TIME-COST OPTIMIZATION

Time and cost are the two most important factors to be considered in every construction project. In order to maximize the profit, both the client and contractor would strive to minimize the project duration and cost concurrently. In the past, most of the research studies related to construction time and cost assumed time to be constant, leaving the analyses based purely on a single objective of cost. Acknowledging this limitation, an evolutionary-based optimization algorithm known as an ant colony system is applied in this study to solve the multi-objective time-cost optimization problems. In this paper, a model is developed using Visual Basic for Application™ which is integrated with Microsoft Project™. Through a test study, the performance of the proposed model is compared against other analytical methods previously used for time-cost modeling. The results show that the model based on the ant colony system techniques can generate better solutions without utilizing excessive computational resources. The model, therefore, provides an efficient means to support planners and managers in making better time-cost decisions efficiently.