Application Of Evolutionary Algorithms Research Articles

Research on multi-objective optimisation of product form design based on kansei engineering

It is crucial to understand and meet the multi-dimensional affective image needs of users for product form in a user demand-oriented product development model. Multi-objective evolutionary algorithms based on decomposition will be introduced into the field of kansei engineering to carry out research on product form optimisation design based on multi-objective evolutionary algorithms. A constrained multi-objective discrete optimisation model was established using the kansei engineering prediction model constructed through machine learning techniques as the objective function, and a reference vector guided evolutionary algorithm was used to solve it. The superiority of this method was verified by comparing it with other commonly used solving methods in this field. Combining entropy weight method and TOPSIS, select the optimisation design scheme that best meets the multi-dimensional affective needs of users from the obtained pareto set. Taking the train as an example, the proposed method was explained. The results indicate that the optimisation scheme obtained by this method can achieve the improvement and optimisation of product form in multiple affective dimensions. Meanwhile, a comparative study on the applicability of multi-objective evolutionary algorithms in the form optimisation problem of different affective dimensions is carried out to provide reference and suggestions for subsequent product design research.

Application of evolutionary algorithms to design economic flat slab buildings based on the intended function

Numerous studies revealed optimization techniques' applicability in minimizing the costs of reinforced concrete buildings. However, the existing literature has narrowly focused on optimizing buildings with a single function, such as residential or office buildings, hindering the generalization of the results. This paper aims to bridge the gap between optimization and structural engineering by obtaining the minimum-cost design of flat slab buildings with different intended functions. In this context, the optimal designs of 120 alternatives were obtained, considering various spans (4–8 m), live loads (2–10 kPa), and concrete compressive strength (25–40 MPa). The optimization was executed using the evolutionary algorithm provided in Microsoft Excel’s Solver tool. The optimization model permits the utilization of drop panels to resist punching stresses developed from the slab-column interaction. The objective function is the cost of materials and labor involved in constructing floors and columns. The decision variables are the floor dimensions and column configurations in dimensions and reinforcement. The structural constraints were applied per the Egyptian design code (ECP203-2020). Eventually, guidelines were developed to help the designers choose the economic floor system and quantities of materials based on the building's intended function.

EFFECTIVE SET IN GENETIC ALGORITHM FOR BIOINFORMATICS

Evolutionary algorithm research and applications began over 50 years ago. Like other artificial intelligence techniques, evolutionary algorithms will likely see increased use and development due to the increased availability of computation, more robust and available open source software libraries, and the increasing demand for artificial intelligence techniques. As these techniques become more adopted and capable, it is the right time to take a perspective of their ability to integrate into society and the human processes they intend to augment. KEYWORDS: GAs(Genetic Algorithms), Hereditary Calculations.

How Recycled Grade is Economical? An Application of MILP and Evolutionary Algorithms in Intermodal Networks Under Uncertain Demand

How Recycled Grade is Economical? An Application of MILP and Evolutionary Algorithms in Intermodal Networks Under Uncertain Demand

New solutions to a multi-objective benchmark problem of induction heating: an application of computational biogeography and evolutionary algorithms

New solutions to a multi-objective benchmark problem of induction heating: an application of computational biogeography and evolutionary algorithms

Application of high-dimensional multi-objective differential evolutionary algorithm based on global ordering to heat supply pipe network resistance identification

The distribution of resistance coefficients of heat supply pipe networks is the key data guiding the hydraulic balance adjustment of heat networks. Since the heat supply pipe network is composed of many pipe segments and the resistance coefficient of each pipe segment is different during heat supply operation, the identification of the resistance coefficient of the heat supply pipe network is an optimization problem with multiple objective functions. In this paper, a high-dimensional multi-objective differential evolutionary algorithm based on global sorting is developed as a method to identify the resistance coefficients of the heat network and the multi-objective algorithm is applied to the resistance identification of the heat network, and the computational process of resistance identification is improved. The fuzzy mathematical method is applied to the process of resistance identification, and a set of optimal solution sets are generated through the identification of each pipe segment and the optimal solutions are selected from the optimal solution sets based on the fuzzy degree of subordination to solve the problem of determining the optimal solutions. The problem of determining the optimal solution is solved. The results show that compared with the single-objective algorithm, the high-dimensional multi-objective differential evolutionary algorithm based on global sorting produces a uniform and concentrated optimal solution set, and the optimal solution accuracy is higher.

A new proximity metric based on optimality conditions for single and multi-objective optimization: Method and validation

With the widespread application of Evolutionary Algorithms (EAs), their performance needs to be evaluated using more than the usual performance metrics. In the EA literature, various metrics assess the convergence ability of these algorithms. However, many of them require prior knowledge of the Pareto-optimal front. Recently, two Karush–Kuhn–Tucker Proximity Metrics (KKTPMs) have been introduced to measure convergence without needing prior knowledge. One relies on the Augmented Achievement Scalarization Function (AASF) method (AASF-KKTPM), and the other on Benson’s method (B-KKTPM). However, both require specific parameters and reference points, making them computationally expensive. In this paper, we introduce a novel version of KKTPM applicable to single-, multi-, and many-objective optimization problems, utilizing the Penalty-based Boundary Intersection (PBI) method (PBI-KKTPM). Additionally, we introduce an approximate approach to reduce the computational burden of solving PBI-KKTPM optimization problems. Through extensive computational experiments across 23 case studies, our proposed metric demonstrates a significant reduction in computational cost, ranging from 20.68% to 60.03% compared to the computational overhead associated with the AASF-KKTPM metric, and from 16.48% to 61.15% compared to the computational overhead associated with the B-KKTPM metric. Noteworthy features of the proposed metric include its independence from knowledge of the true Pareto-optimal front and its applicability as a termination criterion for EAs. Another feature of the proposed metric is its ability to deal with black box problems very efficiently.

A dedicated application of evolutionary algorithms: synchrotron X-ray radiation optimization based on an in-vacuum undulator

Following the rapid growth in accelerator-based light sources research since the mid of 20th century, miscellaneous third generation synchrotron radiation (SR) facilities such as SSRL, APS, ESRF, PETRA-III, and SPring-8 have come into existence. These SR source facilities provide 1020–1025 photons/s/mrad2/mm2/0.1%BW peak brightness within the photon energy range of 10–105 eV. Since different measurement techniques are utilized at X-ray beamlines of SR facilities, many kinds of insertion devices (i.e., undulators and wigglers) and optical components (e.g., high-resolution monochromators, double-crystal monochromators, lenses, mirrors, etc.) are employed for each experimental setup as a matter of course. Under the circumstances, optimization of a synchrotron beamline is a big concern for many scientists to ensure required radiation characteristics (i.e., photon flux, spot size, photon energy, etc.) for dedicated user experiments. In this respect, an in-vacuum hybrid undulator driven by a 6 GeV synchrotron electron beam is optimized using evolutionary algorithms (EA). Finally, it is shown that EA results are well consistent with both the literature and the analytical calculations, resulting in a promising design estimation for beamline scientists.

Application of soft computing and evolutionary algorithms to estimate hydropower potential in multi-purpose reservoirs

Hydropower is a clean and efficient technology for producing renewable energy. Assessment and forecasting of hydropower production are important for strategic decision-making. This study aimed to use machine learning models, including adaptive neuro-fuzzy inference system (ANFIS), gene expression programming, random forest (RF), and least square support vector regression (LSSVR), for predicting hydroelectric energy production. A total of eight input scenarios was defined with a combination of various observed variables, including evaporation, precipitation, inflow, and outflow to the reservoir, to predict the hydroelectric energy produced during the experimental period. The Mahabad reservoir near Lake Urmia in the northwest of Iran was selected as a study object. The results showed that a combination of hydroelectric energy produced in the previous month, evaporation, and outflow from the dam resulted in the highest prediction performance using the RF model. A scenario that included all input variables except the precipitation outperformed other scenarios using the LSSVR model. Among the models, LSSVR exerted the highest prediction performance for which RMSE, MAPE, and NSE were 442.7 (MWH), 328.3 (MWH), and 0.85, respectively. The results showed that Harris hawks optimization (HHO) (RMSE = 0.2 WMH, MAPE = 10 WMH, NSE = 0.90) was better than particle swarm optimization (PSO) (RMSE = 0.2 WMH, MAPE = 10 WMH, NSE = 0.90) in optimizing ANFIS during the prediction. The results of Taylor’s diagram indicated that the ANFIS-HHO model had the highest accuracy. The findings of this study showed that machine learning models can be used as an essential tool for decision-making in sustainable hydropower production.

A review of practice of using evolutionary algorithms for neural network synthesis and training

The object of this research is the application of evolutionary algorithms for the synthesis and training of neural networks. The paper aims to select and review the existing experience on using evolutionary algorithms as competitive methods to conventional approaches in neural network training and creation, and to evaluate such existing solutions for further development of this field. The essence of the obtained results lies in the successful application of genetic algorithms in conjunction with neural networks to optimize parameters, architecture, and weight coefficients of the networks. The genetic algorithms allowed improving the performance and accuracy of neural networks, especially in cases where backpropagation algorithms faced difficulties in finding optimal solutions. These results can be attributed to the fact that genetic algorithms are efficient methods for global optimization in parameter space. They help avoid local minima and discover more reliable and stable solutions. The obtained findings can be practically utilized to enhance the performance and quality of neural networks in various classification and prediction tasks. The use of genetic algorithms enables the selection of optimal weight coefficients, network connections, and identification of significant features from the dataset. However, they come with the limitation of additional time costs for evaluating the entire population according to the selection criteria. It is worth noting that the application of genetic algorithms is not a universal method for all tasks, and the algorithm parameters should be individually tuned for each specific problem. Further research could focus on refining the combination methods of genetic algorithms and neural networks, as well as exploring their application in new domains and tasks.

Modeling emergence of Wolbachia toxin-antidote protein functions with an evolutionary algorithm.

Evolutionary algorithms (EAs) simulate Darwinian evolution and adeptly mimic natural evolution. Most EA applications in biology encode high levels of abstraction in top-down population ecology models. In contrast, our research merges protein alignment algorithms from bioinformatics into codon based EAs that simulate molecular protein string evolution from the bottom up. We apply our EA to reconcile a problem in the field of Wolbachia induced cytoplasmic incompatibility (CI). Wolbachia is a microbial endosymbiont that lives inside insect cells. CI is conditional insect sterility that operates as a toxin antidote (TA) system. Although, CI exhibits complex phenotypes not fully explained under a single discrete model. We instantiate in-silico genes that control CI, CI factors (cifs), as strings within the EA chromosome. We monitor the evolution of their enzymatic activity, binding, and cellular localization by applying selective pressure on their primary amino acid strings. Our model helps rationalize why two distinct mechanisms of CI induction might coexist in nature. We find that nuclear localization signals (NLS) and Type IV secretion system signals (T4SS) are of low complexity and evolve fast, whereas binding interactions have intermediate complexity, and enzymatic activity is the most complex. Our model predicts that as ancestral TA systems evolve into eukaryotic CI systems, the placement of NLS or T4SS signals can stochastically vary, imparting effects that might impact CI induction mechanics. Our model highlights how preconditions and sequence length can bias evolution of cifs toward one mechanism or another.

Learning Adaptive Differential Evolution by Natural Evolution Strategies

Adaptive parameter control is critical in the design and application of evolutionary algorithm (EA), so does in differential evolution. In the past decade, many adaptive evolutionary algorithms have been proposed, in which online information collected until current generation during the evolutionary search procedure is used to determine the algorithmic parameters for the next generation. Recent studies often assume that the algorithmic parameters follow some distributions, while the distributions' parameters (called hyper-parameters) are updated by the collected information. Performances of these adaptive EAs depend highly on the hyper-parameters. Notice that the experiences obtained from optimizing some related problems could provide useful guidelines on how to adaptively control the distributions' parameters. However, few existing studies sufficiently used such experiences. To fill the gap, we propose a general framework for adaptive parameter control by modeling its evolution procedure as a Markov decision process. In the framework, a neural network is employed to act as the controller. The natural evolution strategies is applied to train the neural network. The proposed framework is applied on two well-known differential evolutions (DEs), namely JADE and LSHADE. By incorporating the learned controller, two DEs, named JADE/AC and LSHADE/AC, are formed. Experimental results on the CEC 2018 benchmark suite show that in general JADE/AC and LSHADE/AC perform significantly better than their counterparts. Moreover, in comparison with some well-known EAs including three suggested best DEs in a review paper (including LSHADE, cDE and CoBiDE), the championship algorithm in the CEC 2018 competitions, a recently-developed learnable DE and recently proposed DEs, our study shows that LSHADE/AC performs the best amongst them without sacrificing much computation time.

Method for Parameter Tuning of Hybrid Optimization Algorithms for Problems with High Computational Costs of Objective Function Evaluations

In this paper, the authors focus on presenting the methodology for tuning optimization algorithm parameters, with a special focus on evolutionary algorithm applications. The problem considered concerns the phenomenon of nonlinear buckling of the automotive shock absorber, which itself is solved using a commercial application of the finite element method (FEM) simulation. These analyses are usually time-consuming; therefore, the authors decided to use a surrogate model, which mimics the behavior of the actual nonlinear FEM simulation. Surrogate modeling (metamodeling) is utilized to drastically shorten the simulation time, and thus study numerous algorithm parameter combinations, allowing for tuning them and providing a robust and efficient tool for optimization. The example shown in this paper is related to the minimization of the shock absorber weight, taking into account the stability of the system. The presented method can be used in any optimization problem where the high computational cost of objective function evaluations prevents tuning of the algorithm parameters.

A Survey on Search Strategy of Evolutionary Multi-Objective Optimization Algorithms

The multi-objective optimization problem is difficult to solve with conventional optimization methods and algorithms because there are conflicts among several optimization objectives and functions. Through the efforts of researchers and experts from different fields for the last 30 years, the research and application of multi-objective evolutionary algorithms (MOEA) have made excellent progress in solving such problems. MOEA has become one of the primary used methods and technologies in the realm of multi-objective optimization. It is also a hotspot in the evolutionary computation research community. This survey provides a comprehensive investigation of MOEA algorithms that have emerged in recent decades and summarizes and classifies the classical MOEAs by evolutionary mechanism from the viewpoint of the search strategy. This paper divides them into three categories considering the search strategy of MOEA, i.e., decomposition-based MOEA algorithms, dominant relation-based MOEA algorithms, and evaluation index-based MOEA algorithms. This paper selects the relevant representative algorithms for a detailed summary and analysis. As a prospective research direction, we propose to combine the chaotic evolution algorithm with these representative search strategies for improving the search capability of multi-objective optimization algorithms. The capability of the new multi-objective evolutionary algorithm has been discussed, which further proposes the future research direction of MOEA. It also lays a foundation for the application and development of MOEA with these prospective works in the future.

Application of Evolutionary Algorithms in Social Networks: A Comparative Machine Learning Perspective

Application of Evolutionary Algorithms in Social Networks: A Comparative Machine Learning Perspective

Engineering applications of multi-objective evolutionary algorithms: A test suite of box-constrained real-world problems

Traditionally, the performance of novel multi-objective optimization algorithms is evaluated on artificial test problems, which are constructed under the consideration of some features observed in real-world problems. However, artificial test problems do not possess all the properties that real-world applications have. Therefore, the evaluation of algorithms on artificial test problems can overestimate the conclusions of an algorithm regarding its performance in real-world applications. In this respect, this paper presents a collection of multi-objective real-world problems taken from different disciplines. The multi-objective problems are suggested to complement the performance evaluation of evolutionary algorithms considering real-life applications without the need to be an expert on the concerned disciplines in which they lie. This study analyzes the conflict between objectives from the correlation point of view of each real-world problem. Notably, this investigation inquires on the real-world multi-objective problems regarding the Pareto front shapes, which have been one of the main difficulties for selection mechanisms in multi-objective evolutionary algorithms. Additionally, the performance of nine state-of-the-art multi-objective evolutionary algorithms based on the main principles of evolutionary computation is analyzed. Although it is impractical to evaluate all the existing algorithms available in the multi-objective evolutionary community, this investigation gives insights into the performance of the main multi-objective approaches considered in this study.

Application of Multiobjective Optimization to Provide Operational Guidance for Allocating Supply among Multiple Sources

This study is motivated by multipleobjective optimization in short-term water management for a regional water utility. Although an increasing application of multi-objective evolutionary algorithm (MOEA) has been reported in the literature, we are not aware of its use for short-term water management by water utilities with diverse supply sources. This study presents an innovative practice for determining monthly resource allocation from multiple water supply sources that consider multiple objectives, including deviation from budgeted production, under or overutilization of a given portfolio of resources, and total cost of water production. This method is comprised of a simulation model, namely a production allocation model (PAM) and a MOEA. The decision variables of the MOEA optimization problem are monthly groundwater production from two groundwater wellfields. TheMOEA is used to search for Pareto optimal solutions across different objectives and the PAM uses MOEA output and considers operational constraints to determine water production from the other four supply sources in the decision horizon. Stochastic demand and supply realizations were generated to capture a wide range of uncertainties which were then sampled by a Latin Hyper Cube to make the computation tractable. A parallel computing environment was used to implement this near real-time decision support tool, providing timely guidance for water resources managers. One major difference between this study and many reported in the literature is that the MOEA was used to find Pareto solutions for each demand-supply realization rather than the entire ensemble. This setup allows water resources managers to explicitly explore Pareto solutions based on different supply and demand outlooks. The application of the innovative practice is demonstrated for a regional wholesale water supply utility, Tampa Bay Water, on the west coast of Florida in the United States. One additional advantage of MOEA-assisted planning is that it allows water managers to combine expert judgments and institutional knowledge in identifying solutions. A comparison between MOEA-assisted monthly production planning and heuristic planning reveals that the potential impact of short-term operations, e.g., deviation from budgeted production, is fully considered in a systematic approach. The proposed method can be applied to other regions with similar challenges in water resources management.

Time-Domain Structural Damage Identification Using Ensemble Bagged Trees and Evolutionary Optimization Algorithms

This paper presents a two-step vibration-based strategy for damage identification of framed structures using ensemble bagged trees known as a well-known supervised machine learning (ML) paradigm in conjunction with evolutionary optimization algorithms. The proposed model incorporates the actual response, wavelet coefficients, and wavelet energy to extract damage-sensitive features from the time-domain of the measured and simulated signals. Unlike available studies in this scope, the key objective of this research is to identify damage with a localization precision down to a single structural member, rather than limiting the evaluation to the group of elements. In order to increase the training performance in contributing to extremely large datasets with numerous class labels, the proposed strategy involves the artificial generation of features. Additionally, a modified genetic algorithm is proposed for fast damage localization. It is deduced that the damage locations are confidently detected within a fast computational time. Subsequently, damage identification is followed by the application of evolutionary optimization algorithms. For comparison purpose, the employment of the water cycle optimization algorithm (WCA) is comparatively investigated with the other three state-of-the-art optimizers, i.e., particle swarm optimization (PSO), imperialist competitive algorithm (ICA), and differential evolution algorithm (DE). The numerical and experimental validation studies evidence satisfactorily reliable identification results with no false detection in dealing with multiple damage scenarios in large-scale and real-world applications. It is concluded that developing the most damage-sensitive features and using the proposed data fusion strategy lead to informative features with a reasonably small size and significantly improve the ML performance.

An enhanced surrogate-assisted differential evolution for constrained optimization problems

The application of evolutionary algorithms (EAs) to complex engineering optimization problems may present difficulties as they require many evaluations of the objective functions by computationally expensive simulation procedures. To deal with this issue, surrogate models have been employed to replace those expensive simulations. In this work, a surrogate-assisted evolutionary optimization procedure is proposed. The procedure combines the differential evolution method with a $$k$$ -nearest neighbors (k-NN) similarity-based surrogate model. In this approach, the database that stores the solutions evaluated by the exact model, which are used to approximate new solutions, is managed according to a merit scheme. Constraints are handled by a rank-based technique that builds multiple separate queues based on the values of the objective function and the violation of each constraint. Also, to avoid premature convergence of the method, a strategy that triggers a random reinitialization of the population is considered. The performance of the proposed method is assessed by numerical experiments using 24 constrained benchmark functions and 5 mechanical engineering problems. The results show that the method achieves optimal solutions with a remarkably reduction in the number of function evaluations compared to the literature.

Optimization of active power dispatch considering unified power flow controller: application of evolutionary algorithms in a fuzzy framework

Optimization of active power dispatch considering unified power flow controller: application of evolutionary algorithms in a fuzzy framework