Evolutionary Optimization Model Research Articles

Genetic algorithms as a tool for the formation and evolution of trading strategies in the securities market

Subject. This article deals with the tools for the formation and evolution of trading strategies in the securities market. Objectives. The article aims to develop a mathematical framework and a formalized algorithm for the formation and evolution of trading strategies in the securities market. Methods. For the study, I used a genetic algorithm as an evolutionary optimization model. Results. The article presents an original mathematical description of a tool for the formation and evolution of strategies using genetic algorithms and a formalized algorithm sufficient for implementation in general-purpose programming languages. Conclusions and Relevance. The results obtained are the basis for further research in the field of autonomous evolution of trading strategies, and the described models will allow further research on the basis of specific software tools. The work is of practical value for private and institutional investors, for whom the trading strategy in the securities market is one of the fundamental aspects of their activities.

Generative urban design: A systematic review on problem formulation, design generation, and decision-making

Urban design is the process of designing and shaping the physical forms of cities, towns, and suburbs. It involves the arrangement and design of street systems, groups of buildings, public spaces, and landscapes, to make the urban environment performative and sustainable. The typical design process, reliant on manual work and expert experience has unavoidable low efficiency in generating high-performing design solutions due to the involvement of complex social, institutional, and economic contexts and the trade-off between conflicting preferences of different stakeholder groups. Taking advantage of artificial intelligence (AI) and computational capacity, generative urban design (GUD) has been developed as a trending technical direction to narrow the gaps and produce design solutions with high efficiency at early design stages. It uses computer-aided generative methods, such as evolutionary optimization and deep generative models, to efficiently explore complex solution spaces and automatically generate design options that satisfy conflicting objectives and various constraints. GUD experiments have attracted much attention from academia, practitioners, and public authorities in recent years. However, a systematic review of the current stage of GUD research is lacking. This study, therefore, reports on a systematic investigation of the existing literature according to the three key stages in the GUD process: (1) design problem formulation, (2) design option generation, and (3) decision-making. For each stage, current trends, findings, and limitations from GUD studies are examined. Future directions and potential challenges are discussed and presented. The review is highly interdisciplinary and involves articles from urban study, computer science, social science, management, and other fields. It reports what scholars have found in GUD experiments and organizes a diverse and complicated technical agenda into something accessible to all stakeholders. The results and discoveries will serve as a holistic reference for GUD developers and users in both academia and industry and form a baseline for the field of GUD development in the coming years.

Corrigendum to “Bridge maintenance planning framework using machine learning, multi-attribute utility theory and evolutionary optimization models” [Automation in Construction, September 2022, Volume 141, 104460

Corrigendum to “Bridge maintenance planning framework using machine learning, multi-attribute utility theory and evolutionary optimization models” [Automation in Construction, September 2022, Volume 141, 104460

Bridge maintenance planning framework using machine learning, multi-attribute utility theory and evolutionary optimization models

Although various tools and procedures have been developed to help transportation engineers objectively evaluate bridge maintenance needs, selecting and scoping projects still relies on engineering judgment. The present paper intends to help engineers evaluate and maintain bridges by developing a comprehensive bridge maintenance planning framework (BMPF) within financial and performance constraints. The paper's objectives are to maximize the performance condition level of bridges and to minimize the maintenance cost by optimally planning the maintenance treatments. The framework includes bridge performance impact assessment, machine learning models, multi-attribute utility theory ranking model and genetic algorithm optimization model. The study analysed 95 bridges in a network with an 84% accuracy machine learning model prediction. Decision-makers' preferences were considered to rank all bridges using Multi-Attribute Utility Theory. 19 bridges were then chosen for maintenance based on budget and performance using a genetic algorithm model. The BMPF was observed to improve project productivity, reduce down time, and improve bridge inventory condition. Future research can explore the use of other optimization approaches and also include traffic flow and construction cost analysis for a better maintenance cost estimation. The machine learning model for performance prediction can be enhanced by utilizing different techniques.

A mathematical optimisation model for the design and detailing of reinforced concrete beams

An evolutionary metaheuristic optimization model for sizing reinforced concrete beams is presented. The proposed optimization model transcends ones in the literature as it is able to consider characteristic concrete strength (fck), cross-section area and reinforcement bars' diameters as discrete design variables, thus representing a more realistic model. The goal is to minimize construction costs via configuring cross-sectional dimensions, determining reinforcement layouts, and defining fck. The design constraints formulated in the mathematical model proposed are related to structural integrity, considering ultimate limit state, serviceability limit state, and good construction practices. A finite element method program was developed to obtain the stresses and strains of beams, geometries, and load forces. Additionally, a longitudinal reinforcement database generator was developed, ensuring that the reinforcement layouts generated are within codes of practice. A Genetic Algorithm was adopted to solve the resulting optimisation problem. Results of case studies demonstrate that the cost variance implications are directly related to the reinforcement detailing arrangements, with 3.63% to 17.07% improvements in costs that are achieved when compared with other studies in the literature.

Green building technologies adoption process in China: How environmental policies are reshaping the decision-making among alliance-based construction enterprises?

To expedite green building technologies (GBTs) adoption among construction enterprises, the Chinese government has launched a series of environmental policies that encourage the establishment of green building alliances. By building an evolutionary game optimization model, this study reveals the game strategy changes of multiple stakeholders and the impact of environmental policies on GBTs adoption among alliance-based construction enterprises. On this basis, the case study and simulation method are used to simulate the impact of implementation strength for environmental policies on the decision-making of stakeholders in alliances. The simulation results show that the strength of different environmental policies should reach a certain threshold (over 0.5). Comparatively speaking, the effect of green subsidy policies is usually superior to the impact of environmental tax policies; and the higher the ratio of market participants in the carbon emission trading, the better and more complete the GBTs adoption in alliances. Moreover, a single policy is more affected by the internal environment in alliances, but policy mixes can be most effective to promote GBTs adoption among construction enterprises. Therefore, the combination of environmental taxes, green subsidies and carbon trading will be the better policy instruments to develop GBTs in green building alliances.

A decomposition and multi-objective evolutionary optimization model for suspended sediment load prediction in rivers

Suspended sediment load (SSL) estimation is essential for both short- and long-term water resources management. Suspended sediments are taken into account as an important factor of the service life of hydraulic structures such as dams. The aim of this research is to estimat SSL by coupling intrinsic time-scale decomposition (ITD) and two kinds of DDM, namely evolutionary polynomial regression (EPR) and model tree (MT) DDMs, at the Sarighamish and Varand Stations in Iran. Measured data based on their lag times are decomposed into several proper rotation components (PRCs) and a residual, which are then considered as inputs for the proposed model. Results indicate that the prediction accuracy of ITD-EPR is the best for both the Sarighamish (R 2 = 0.92 and WI = 0.96) and Varand (R 2 = 0.92 and WI = 0.93) Stations (WI is the Willmott index of agreement), while a standalone MT model performs poorly for these stations compared with other approaches (EPR, ITD-EPR and ITD-MT) although peak SSL values are approximately equal to those by ITD-EPR. Results of the proposed models are also compared with those of the sediment rating curve (SRC) method. The ITD-EPR predictions are remarkably superior to those by the SRC method with respect to several conventional performance evaluation metrics.

Result-based management approach for aquifer restoration problems using a combined numerical simulation – parallel evolutionary optimization model

Result-based management (RBM) has been used as a new management approach for groundwater resources planning. Based on this approach, three levels of results including outputs (aquifer stability), outcomes (positive balance), and impacts (aquifer restoration) have been considered in this study. To monitor these levels, three indicators based on sustainability, filling, and restoration definitions are given to describe the associated RBM aquifer restoration problems. In this study, a combined simulation–optimization model has been developed using a MODFLOW-based numerical simulation model and a parallel evolutionary algorithm of continuous ant colony optimization. The model has been applied to a real case study of Namdan aquifer located in Fars province, Iran, over a planning horizon of 10 years. Three levels of outputs, outcomes, and impacts have been achieved and the effectiveness of RBM approach in groundwater resources management has been assessed. The important observations are: (1) The planning based on the management problems of sustainability, filling, and restoration indicators leads to an increase in the groundwater elevation by 3, 9.47, and 10.61 m at the end of management period in comparison with the beginning of the period; (2) The planning based on the restoration indicator causes the highest level of rise in groundwater elevation and reaches the restoration target as highlighting the significance of a proper management setup to achieve the proposed target; (3) The investigations based on reliability, utility, and vulnerability indicators show the improvement of aquifer restoration status over the results chain. The methodology given in this study can be a novel approach to the realistic management of groundwater resources considering the proposed targets while it can be improved by the involvement of stakeholders and beneficiaries in RBM implementation.

Evolutionary bi-level model for optimizing ticket fares and operations profit of Taiwan high-speed rail

The Taiwan High Speed Rail (THSR) has transformed transportation in western Taiwan. Increasing costs of construction and operation have rendered THSR tickets much more expensive than other forms of transportation. The effects of ticket price benefits on the relevant transportation agency and the distribution of passenger flow have been analyzed to improve the profitability of the THSR. However, relying on analysis of passenger flow information alone may be insufficient to the adjustment of ticket fares. An evolutionary optimization model for maximizing the profits of the transportation agency that considers conflicts between executive decision making and passenger flow was developed herein. To resolve these conflicts, the bi-level planning approach was applied to consider both upper-level and lower-level planning and reflect passenger behavior and transportation profit management. Therefore, developing business plans and strategies that maximize cost efficiency while optimally balancing passenger satisfaction and profitability could improve THSR performance. Results indicated that optimal fare rates should be divided into three groups: 3.1, 3.0, and 4.2 NTD/person-km for long, medium, and short distances, respectively. This tool could be used to model and validate THSR fare adjustments and as a reference for authorities when making policy recommendations.

Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings

Accurate forecast of energy consumption is essential in building energy management. Owing to the variation of outdoor weather condition among different seasons, year-round historical weather profile is needed to investigate its feature thoroughly. Daily weather profiles in the historical database contain various features, while different architecture of deep neural network (DNN) models may be identified suitable for specific featuring training datasets. In this study, an integrated artificial intelligence-based approach, consisting of feature extraction, evolutionary optimization and adaptive DNN model, is proposed to forecast week-ahead hourly building energy consumption. The DNN is the fundamental forecasting engine of the proposed model. Feature extraction of daily weather profile is accomplished through clustering techniques. Genetic algorithm is adopted to determine the optimal architecture of each DNN sub-model. Namely, each featuring cluster of weather profile, along with corresponding time signature and building energy consumption, is adopted to train one DNN sub-model. Therefore, the structure, activation function and training approach of DNN sub-models are adaptive to diverse featuring datasets in each cluster. To evaluate the effectiveness of the proposed predictive model, it is implemented on a real office building in the United Kingdom. Mean absolute percentage error of the training and testing cases of the proposed predictive model is 2.87% and 6.12%, which has a 24.6% and 11.9% decrease compared to DNN model with a fixed architecture. With the latest weather forecast, the devised adaptive DNN model can provide accurate week-ahead hourly energy consumption prediction for building energy management system.

Optimization based feasibility analysis for Energy Conservation Building Code compliance of opaque wall assemblies in different climatic zones of India

Thermal performance of building envelopes is the determinant factor of building energy consumption. The total built up area of commercial buildings in India is expected to touch 1.9 Billion Sq. m. by 2030 from the 2015 figure of 847 Million Sq. m., a growth of three times. According to Central Electricity Authority of India the projected energy consumption load for 2030 is five times that of the current load in the region. Energy Conservation Building Code (ECBC) for commercial buildings was first launched in 2007 towards establishing new benchmarks for energy efficient buildings in India. There has been inadequate implementation of ECBC in the country and the success of these codes, policies and regulations depends on the market response to be self-sustaining in the long term. This research uses the evolutionary optimization model for the feasibility analysis of ECBC and incremental ECBC code compliance, in various climatic zones of India towards wider acceptability by the building industry with respect to the thermal transmittance of opaque wall assemblies in building envelope for different typologies of commercial buildings using the available materials in the Indian context / market.

Hormones as adaptive control systems in juvenile fish.

ABSTRACTGrowth is an important theme in biology. Physiologists often relate growth rates to hormonal control of essential processes. Ecologists often study growth as a function of gradients or combinations of environmental factors. Fewer studies have investigated the combined effects of environmental and hormonal control on growth. Here, we present an evolutionary optimization model of fish growth that combines internal regulation of growth by hormone levels with the external influence of food availability and predation risk. The model finds a dynamic hormone profile that optimizes fish growth and survival up to 30 cm, and we use the probability of reaching this milestone as a proxy for fitness. The complex web of interrelated hormones and other signalling molecules is simplified to three functions represented by growth hormone, thyroid hormone and orexin. By studying a range from poor to rich environments, we find that the level of food availability in the environment results in different evolutionarily optimal strategies of hormone levels. With more food available, higher levels of hormones are optimal, resulting in higher food intake, standard metabolism and growth. By using this fitness-based approach we also find a consequence of evolutionary optimization of survival on optimal hormone use. Where foraging is risky, the thyroid hormone can be used strategically to increase metabolic potential and the chance of escaping from predators. By comparing model results to empirical observations, many mechanisms can be recognized, for instance a change in pace-of-life due to resource availability, and reduced emphasis on reserves in more stable environments.This article has an associated First Person interview with the first author of the paper.

Optimal Scheduling Method of Transport Path in Coastal Port International Logistics Park

Tang, X., 2019. Optimal scheduling method of transport path in coastal port international logistics park. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 1125–1131. Coconut Creek (Florida), ISSN 0749-0208.In order to improve the dynamic optimization control ability of coastal port international logistics park transportation and transportation, an optimal scheduling method of coastal port international logistics park transport path based on artificial intelligence path constraint is proposed. The multi-objective evolution method is used to control the path constraint of the transport path optimization scheduling of the coastal port international logistics park, and the bee colony method is used to optimize the route selection of the transport path optimization scheduling of the coastal port international logistics park. The multi-objective Pareto mapping method is used to carry out the path planning and design of the coastal port international logistics park transport. Combined with pheromone guidance method, the adaptive control of transport path optimization scheduling of coastal port international logistics park is carried out, and the bee colony evolutionary optimization model of coastal port international logistics park transport path optimization scheduling is constructed, and the artificial intelligence algorithm of coastal port international logistics park transport path optimization scheduling is constructed according to the colony path constraint optimization method. Artificial intelligence control and adaptive optimization of transport path optimal scheduling in coastal port international logistics park are realized. The simulation results show that this method has good adaptive performance and strong path optimization control ability for the optimal scheduling of transport paths in coastal port international logistics parks.

Integrating industrial waste heat recovery into sustainable smart energy systems

To achieve the EU climate and energy objectives, a transition towards a future sustainable energy system is needed. The integration of the huge potential for industrial waste heat recovery into smart energy system represents a main opportunity to accomplish these goals. To successfully implement this strategy, all the several stakeholders' conflicting objectives should be considered. In this paper an evolutionary multi-objective optimization model is developed to perform a sustainability evaluation of an energy system involving an industrial facility as the waste heat source and the neighbourhood as district heating network end users. An Italian case study of heat recovery from a steel casting facility shows how the model allows to properly select the district heating network set of users to fully exploit the available waste energy. Design directions such as the thermal energy storage capacity can be also provided. Moreover, the model enables the analysis of the trade-off between the stakeholders’ different perspectives, allowing to identify possible win-win solutions for both the industrial sector and the citizenship.

Global Robust Control Model of Friction Stir Welding of Aluminum Alloy Based on Main Motor Power Output Prediction

Aiming at the softening problem of 7075 superhard aluminium welded joints such as hot cracks and gas holes in traditional fusion welding process, a dynamic robust control model based on the relationship between friction stir welding process parameters and welded joint strength is established. According to the non-linear relationship between global motion estimation and welded strength parameters, a dynamic robust control model of stirring needle motion is established, and the physical parameters such as elongation after fracture, tensile strength and hardness are calculated. A robust dynamic evolutionary optimization model of welding process parameters is established in the future to realize real-time optimization control of process parameters. On the basis of global motion control, the electric power output of the friction stir welding main motor is predicted according to the required line energy input of the stirring needle motion characteristics, and the real-time optimal control method of the motor electric power based on the expected thermal energy input of the welding process is established. A series of strength tests of welded joints based on the model show that the predicted results of the model can meet the needs of engineering and have practical value in engineering.

COSUM: Text summarization based on clustering and optimization

AbstractText summarization is a process of extracting salient information from a source text and presenting that information to the user in a condensed form while preserving its main content. In the text summarization, most of the difficult problems are providing wide topic coverage and diversity in a summary. Research based on clustering, optimization, and evolutionary algorithm for text summarization has recently shown good results, making this a promising area. In this paper, for a text summarization, a two‐stage sentences selection model based on clustering and optimization techniques, called COSUM, is proposed. At the first stage, to discover all topics in a text, the sentences set is clustered by using k‐means method. At the second stage, for selection of salient sentences from clusters, an optimization model is proposed. This model optimizes an objective function that expressed as a harmonic mean of the objective functions enforcing the coverage and diversity of the selected sentences in the summary. To provide readability of a summary, this model also controls length of sentences selected in the candidate summary. For solving the optimization problem, an adaptive differential evolution algorithm with novel mutation strategy is developed. The method COSUM was compared with the 14 state‐of‐the‐art methods: DPSO‐EDASum; LexRank; CollabSum; UnifiedRank; 0–1 non‐linear; query, cluster, summarize; support vector machine; fuzzy evolutionary optimization model; conditional random fields; MA‐SingleDocSum; NetSum; manifold ranking; ESDS‐GHS‐GLO; and differential evolution, using ROUGE tool kit on the DUC2001 and DUC2002 data sets. Experimental results demonstrated that COSUM outperforms the state‐of‐the‐art methods in terms of ROUGE‐1 and ROUGE‐2 measures.

Structural Optimization of Hatch Cover Based on Bi-directional Evolutionary Structure Optimization and Surrogate Model Method

Weight reduction has attracted much attention among ship designers and ship owners. In the present work, based on an improved bi-directional evolutionary structural optimization (BESO) method and surrogate model method, we propose a hybrid optimization method for the structural design optimization of beam-plate structures, which covers three optimization levels: dimension optimization, topology optimization and section optimization. The objective of the proposed optimization method is to minimize the weight of design object under a group of constraints. The kernel optimization procedure (KOP) uses BESO to obtain the optimal topology from a ground structure. To deal with beam-plate structures, the traditional BESO method is improved by using cubic box as the unit cell instead of solid unit to construct periodic lattice structure. In the first optimization level, a series of ground structures are generated based on different dimensional parameter combinations, the KOP is performed to all the ground structures, the response surface model of optimal objective values and dimension parameters is created, and then the optimal dimension parameters can be obtained. In the second optimization level, the optimal topology is obtained by using the KOP according to the optimal dimension parameters. In the third optimization level, response surface method (RSM) is used to determine the section parameters. The proposed method is applied to a hatch cover structure design. The locations and shapes of all the structural members are determined from an oversized ground structure. The results show that the proposed method leads to a greater weight saving, compared with the original design and genetic algorithm (GA) based optimization results.

Modeling and intelligent optimization of social collective behavior with online public opinion synchronization

Today, the public opinion synchronization on the network platform is becoming one of important issues worthy of careful study. In this paper, we take synchronization evolution phenomenon as an objective and adopt artificial bee colony (ABC) to evaluate network synchronization effects with optimization theory to find out an appropriate network structure. Firstly, we use the Kuramoto oscillators as a metaphor of the social system collective behavior. Secondly, combined with the social network characteristics obtained from the data of Sina Micro-Blog, a synchronization evolution model of Internet public opinion based on Kuramoto one is established. Subsequently, evolutionary multi-objective optimization model is set up and the ABC method is used to optimize the level of network synchronization, the synchronization starting time and the cost of public opinion synchronization. Finally, case analysis on “Double Eleven” Internet Marketing as well as Cadmium Poisoned Rice Event demonstrates that the synchronization performance of weak coupled system can be enhanced by offering reasonable configuration of the connection cost and the synchronization duration cost. In addition, a certain degree of increase in input cost can promote the synchronization performance and extend the synchronization duration significantly.

Bilevel mixed-integer nonlinear programming for integrated scheduling in a supply chain network

An integrated scheduling problem under a make-to-order supply chain network is addressed. This problem considers integrated production and transportation scheduling with realistic supply chain features such as unrelated parallel shop and product batch-based transportation. The mathematical model for this problem is presented, which is formulated as a bilevel mixed-integer nonlinear program. A novel bilevel evolutionary optimization model based on memetic algorithm is proposed to resolve this problem because the problem is hard-to-tackle for mathematical programming techniques and traditional intelligent techniques. The effectiveness of the proposed optimization model is validated through a series of numerical experiments. The experimental results also confirmed that the proposed optimization model is superior to other three intelligent optimization models.

An integrated beam-plate structure multi-level optimal design framework based on bi-directional evolutionary structural optimization and surrogate model

Lots of endeavors have been made to apply optimization techniques to real design problems for various engineering beam-plate structures, however, due to the limitation of traditional topological form and the difficulty of finding the optimal topology in numerous optional design plans, many beam-plate structure designs are not the optimal solution but only a feasible solution. This paper proposes an integrated optimal design framework for beam-plate structure based on combining bi-directional evolutionary structural optimization (BESO) and surrogate model method, which covers three optimization levels, as dimension optimization, topology optimization and section optimization. BESO is used for topology optimization. In order to deal with beam-plate structures, the traditional BESO method is improved by using cubic box as the unit cell instead of solid unit to construct periodic lattice structure. Requirements for the framework are discussed based on the features of beam-plate structure design process first. The proposed framework consists of automated finite element modeling module, structure optimization module and post-processing module. Usefulness of the designed framework is examined through a cantilever beam structure design.