Optimization Of Complex Systems Research Articles

Дуальний метод програмування

Задача оптимізації трактується як вибір такого поєднання аргументів (незалежних змінних), яке при заданих зовнішніх впливах та обмеженнях породжує екстремум цільової функції. Цільова функція відбиває поняття критерію оптимізації. За наявності кількох критеріїв цільова функція має сенс скалярної згортки критеріїв. Сутність поняття оптимізації полягає у екстремізації цільової функції. Це стосується і задач прийняття рішень, і задач керування, і інших предметних областей. Запропоновано нелокальний метод математичного програмування, який дає змогу зменшити кількість необхідних обчислень цільової функції. При оптимальному проектуванні, особливо багатокритеріальному, цільові функції розраховуються на складних алгоритмах, що вимагають великих обчислювальних ресурсів і обчислювального часу. Запропонований метод дуального програмування актуальний для комп'ютерної оптимізації складних систем.

Optimal Design of Periodic Honeycomb Plate with Unit Cell Structure Based on Genetic Algorithm

Composite honeycomb sandwich plates have been widely used in aerospace, ships, construction bridges, machinery, stationery, and other industries. In order to improve the performance and configuration, taking the structural dynamic characteristics of periodic honeycomb plate as the research object, a structural optimization design method based on natural frequency and stiffness was proposed with the goal of weight reduction. The geometric parameters of the structure system were trained by the multiobjective optimization genetic algorithm (MOGA), and the Pareto optimal solution of variable combination was obtained. This paper presented a decoupling method for complex system optimization design based on dynamic performance from the perspective of basic unit, which could solve the coordination problem of vibration stability and weight reduction in periodic honeycomb plate structure optimization design. It has reference significance for the similar composite material frame base structure design.

Optimization of maximum power density output for proton exchange membrane fuel cell based on a data-driven surrogate model

Operating conditions are of great significance for proton exchange membrane fuel cells (PEMFCs) and directly determine the output performance of PEMFC. In this paper, an optimization framework is proposed, which is combining a neural network data-driven surrogate model and a stochastic optimization algorithm to achieve multi-variable global optimization, and optimizes the operating conditions for enhancing the power density of the PEMFC. A numerical model of the PEMFC is constructed as the source of the database, and the database is used to train a data-driven surrogate model based on radial basis functions (RBF), which is a typical feed-forward neural network. Then the surrogate model is fed into a particle swarm optimization (PSO) algorithm to obtain the optimal solution for the best combination of operating conditions. Results show that the surrogate model can accurately predict the output voltage of the PEMFC model, where the squared correction factor (R-square) and the mean percentage error of the test set are 0.99638 and 4.4686% respectively. And the optimization framework using a combination of data-driven surrogate model and stochastic optimization algorithm can obtain the maximum power density of 0.6097 W cm−2, with a relative error of only 1.5321% to the PEMFC model results. The result shows that the framework can effectively handle the multivariate optimization of complex systems.

A Two-stage Successive Approximation Method of Dynamic Programming for Operations of Cascaded Hydropower Plants with Navigation Demand

The operations of cascaded hydropower plants have always been recognized as a complex system optimization problem. The difficulty of modelling and optimization solution is further aggravated when comprehensive water demands for power generation, navigation and ecology are considered. This study develops an optimization model for operations of cascaded hydropower plants with navigation demands, and proposes a two-stage successive approximation method of dynamic programming. In the model, the navigation demand is considered as a constraint. The first stage is to optimize the operation of cascaded hydropower plants with no consideration of navigation demand. The optimized result is taken as an initial solution of the last stage. Moreover, the navigation constraint is introduced in the solution procedure to obtain a reasonable operation scheme. The method is implemented on operations of a cascaded hydropower plants with navigation task in Southwest China. The results show that the navigation demand can be satisfied by coordination of cascaded hydropower plants. Moreover, this constraint has an effect on the monthly distribution of total energy production during one year. In addition, the computing efficiency and solution accuracy are analysed by using different discrete steps.

Optimization of Extraction Process Based on Neural Network

Liquid-liquid extraction is a chemical unit operation that utilizes the difference in solubility or distribution ratio of target components in two immiscible solvents to achieve separation, extraction or purification. There are many factors that affect the extraction efficiency, and it is difficult to quickly optimize the process using traditional methods. Artificial neural network is a system structure composed of multiple artificial neuron models, with functions such as self-learning, associative storage and fault tolerance. It can be used for optimization or control of multi-variable complex systems, and has been successfully applied to the extraction process of various products. optimization. This paper discusses the basic situation of artificial neural network, and analyzes the research progress of extraction process optimization based on neural network.

Alternative Water Resources Selection to Supply Drinking Water in Flood Disasters by Multicriteria Decision-Making Techniques (DANP and VIKOR).

Background Emergency is generally caused by natural disaster and infectious disease outbreaks, or it is man-made. Floods are natural phenomena that generally appear in multiple parts of the world. Flooding is one of the most destructive naturally occurring environmental hazards and can cause public, infrastructural, and environmental damage. The purpose of this study is to select alternative water resources for supplying Bandar Abbas in flood disasters by multicriteria decision-making techniques. Methods Information required includes possible water resources alternative for flood, quantitative and qualitative characteristics of the water resources, climatic circumstances, and demographic information used in organizations data and previous studies. After selecting and proposing water resources alternative for Bandar Abbas in flood, the subcriteria were weighed applying DANP (DEMATEL-ANP) techniques and water resources were prioritized with the VIKOR technique. According to the network structure and internal and external dependence of the criteria and subcriteria, the advantages of DANP in calculating weights have been used to adapt to more real-world problems. The VIKOR technique was developed for multicriteria optimization of complex systems. Results After reviewing and extracting the criteria from various studies, 9 main criteria and 44 subcriteria were defined to select water resources in disasters and emergencies. According to field studies and related organizations' information, the proposed water resources for Bandar Abbas to use in flood disasters include humidity, sea (Persian Gulf), Sarkhoon plain, and wastewater treatment plant of Bandar Abbas. Conclusion Results showed that the optimal water resources for Bandar Abbas in flood disasters are the sea and wastewater treatment plant effluent (after advanced treatment). The study proposed appropriate model to select optimal water resources for various natural disasters in different geographical areas. This model can help officials and decision-makers to plan for drinking water supply from disaster-prone areas before disasters occur.

A METHOD FOR CHOOSING A STRATEGY FOR THE BEHAVIOR OF A CELLULAR AUTOMATON WHEN SOLVING THE PROBLEM OF FINDING TARGETS BY A GROUP OF MOVING OBJECTS

Currently, a large number of different mathematical models and methods aimed at solving problems of multidimensional optimization and modeling of complex behavioral systems have been developed. One of the areas of search for solutions is the search for solutions in conditions of incomplete information and the need to take into account changing external factors. Often such problems are solved by the method of complete search. In some conditions, the method of complete search can be significantly improved through the implementation and use of behavioral models of natural formations. Examples of such formations can be group behavior of insects, birds, fish, various flocks, etc. The idea of copying group activity of a shoal of fishes at the decision of problems of joint activity on extraction of food is used in work. The reasoning based on the simulation of the behavior of such a natural object allowed to justify the choice as a mathematical model - cellular automata. The paper examines the key features of such a model. Modeling of his work is carried out, strategies of behavior of group of mobile objects at search of the purposes are developed, key characteristics are investigated and the method of adaptive choice of strategy and change of rules of behavior taking into account features of the solved problem is developed. The search strategy is implemented in the work, which takes into account the need to solve the optimization problem on two parameters. The obtained results testify to the high descriptive possibility of such an approach, the possibility of finding the optimal strategy for the behavior of the cellular automaton and the formalization of the process of selecting the parameters of its operation. A further improvement of this approach can be the implementation of simulation to study the properties of the developed model, the formation of the optimal set of rules and parameters of the machine for the whole set of tasks.

A methodology to improve simulation of multibody systems using estimation techniques

ABSTRACT This paper presents a methodology for state estimation and accuracy improvement of computer simulations of computer aided engineering (CAE) models based on prediction and correction state estimation techniques and sensing. The aim is to simulate the dynamic behaviour of a real system, which can be sensed, and obtain values of states that are not measurable due to economic or technical limitations. This methodology can be applied to both optimization of design processes and on-line control of complex systems. State estimation techniques are currently used only on mathematical models, where the relationships among system variables are expressed by means of mathematical language, making state observer implementation possible but leading to limitations in system modelling and knowledge. Favoured over mathematical models, multibody CAE models (created by means of computer-aided engineering software) have become the essential tool for complex system development, simulation, analysis, optimization and control, such as multibody systems; one of their main advantages is the ease and flexibility in creating and modifying them, allowing the faithful modelling of complex systems.

Using Proactive Maintenance Strategy for Sustainable Electric Power Production in Nigeria

An unpleasant turn of events would compel and prevail upon power plants owners and operators looking for alternative for keeping maintenance activities up and awake. Thus, causing a break away from outdated traditional system originally practiced in maintenance organizations, which affects operations in terms of costs and energy required for sustainable activity in the power production industry; via Generation, Transmission and Distribution. However, the only inevitably obtainable option as per organizational success in the power industry is through a substituent for a failure based system with success based strategy that is concerned with optimization of complex processes, systems or organization by developing, improving and implementing integrated systems of people, money, knowledge, information and equipment, central to manufacturing and production operations. A platform intended to deliver on plant (a) availability (b) reliability and (c) sustainability, which lead to reduced maintenance costs and increased profitability. Keywords: maintenance management; strategy implementation, power sector, performance optimization DOI: 10.7176/IEL/11-3-03 Publication date: October 31 st 2021

SELECTION AND JUSTIFICATION OF THE MATHEMATICAL 6 FACTORS EXPERIMENT PLAN IN THE STUDY OF ICE STARTING QUALITIES

The practical significance of the application of multivariate regression analysis in engineering practice as a necessary step towards improving and optimizing complex systems and processes is presented. The goal and investigation tasks have been actualized to optimize the start-up process of a high-speed subcompact diesel engine. Preparation of the plural factors experiment features of diesel start-up process based on regression analysis and the theory of mathematical design of experiment are presented. Qualitative optimizing parameters of the diesel engine cold start are determined: instantaneous acceleration of the crankshaft dn / dτ at the time of crankshaft cranking by an external energy source close to the time spent on starting the engine with optimal parameters of the starting system and starting energy consumed during the crankshaft cranking by an external energy source. Six main factors influence on certain parameters of the diesel engine start quality have been investigated. The regression equation for assessing the quality of the start-up process is substantiated. The form of regression equation is a full quadratic polynomial for the reproduction of the investigated functions, based on the previous study of some used in the study individual factors influence. The analysis of proposals for rational plan selection of a six-factor experiment to find the coefficients of the second-degree regression polynomial is carried out. The substantiation of the decision regarding the choice of the plan is given. Selection was based on the requirements of the investigation test bench and the conditions for study organizing. Main impact had the predecessors experience of similar investigation and the statistical criteria for different plans of experiment comparison used in mathematical theory of design of experiments. The points of the plan have been defined on a multidimensional cube due to the need to vary on three levels with a uniform step of all 6 factors that were identified as influential. Under the conditions of the available laboratory test bench, the current values of the factors of equivalent cold start temperature, crankshaft-cranking speed and maximum temperature of the glow plugs have a variance of the installation at different points of the plan, and the nature of the factors on energy consumption is unknown in advance. As a rational experimental design for organizing the current study, the Box-Wilson orthogonal central compositional plan was chosen, built by adding plan points on the axes of the factorial space to the full-factorial plan of the lowest order, while maintaining the requirement of orthogonality and symmetry of the plan.

GUIDED HYBRID GENETIC ALGORITHM FOR SOLVING GLOBAL OPTIMIZATION PROBLEMS

Context. One of the leading problems in the world of artificial intelligence is the optimization of complex systems, which is often represented as a nonlinear function that needs to be minimized. Such functions can be multimodal, non-differentiable, and even set as a black box. Building effective methods for solving global optimization problems raises great interest among scientists. Objective. Development of a new hybrid genetic algorithm for solving global optimization problems, which is faster than existing analogues. Methods. One of the crucial challenges for hybrid methods in solving nonlinear global optimization problems is the rational use of local search, as its application is accompanied by quite expensive computational costs. This paper proposes a new GBOHGA hybrid genetic algorithm that reproduces guided local search and combines two successful modifications of genetic algorithms. The first one is BOHGA that establishes a qualitative balance between local and global search. The second one is HGDN that prevents reexploration of the previously explored areas of a search space. In addition, a modified bump-function and an adaptive scheme for determining one of its parameters – the radius of the “deflation” of the objective function in the vicinity of the already found local minimum – were presented to accelerate the algorithm. Results. GBOHGA performance compared to other known stochastic search heuristics on a set of 33 test functions in 5 and 25dimensional spaces. The results of computational experiments indicate the competitiveness of GBOHGA, especially in problems with multimodal functions and a large number of variables. Conclusions. The new GBOHGA hybrid algorithm, developed on the basis of the integration of guided local search ideas and BOHGA and HGDN algorithms, allows to save significant computing resources and speed up the solution process of the global optimization problem. It should be used to solve global optimization problems that arise in engineering design, solving organizational and management problems, especially when the mathematical model of the problem is complex and multidimensional.

Vulnerability Assessment of Deep Reinforcement Learning Models for Power System Topology Optimization

This paper studies the vulnerability of deep reinforcement learning (DRL) models for power systems topology optimization under data perturbations and cyber-attack. DRL has recently solved many complex power system optimization problems. However, it has been practically proven that small perturbations of input data can lead to drastically different control decisions and induce danger. To evaluate and mitigate the security risks of DRL models in power systems, we propose a vulnerability assessment method for such DRL models under noisy data and cyber-attack. In specific, we assess the vulnerability of a DRL model in a way that perturbations are constructed to minimize the model’s performance. Besides, several vulnerability indices are proposed to identify the characteristics of perturbations that may cause malfunction of DRL. Simulations on the 14-bus system and the IEEE 118-bus system for topology optimization are carried out to validate the effectiveness of the proposed vulnerability assessment method. The results show that the performance of DRL models for power systems can be significantly degraded under cyber-attack and data perturbations, especially when a proposed vulnerability index has abnormal values.

Adaptive Genetic Algorithm Renewed by Migration Operator

In the present study, the Genetic Algorithm and the developed Adaptive Genetic Algorithm are used to solve optimization problems faced in modeling of complex systems. While using the Genetic Algorithm and the Adaptive Genetic Algorithm, the most important problem encountered is that these algorithms are prone to getting stuck in local bests. For example, in the complex system data optimization process using the Genetic Algorithm, it has been observed that it is frequently fitted to local bests in a certain period of time. The reasons are that, many mutations cannot be performed in the limited number of iterations, and because the number of individuals is limited, the population is filled with the same set of solutions in a short time. Therefore, the migration operator has been added to the Genetic Algorithm and the Adaptive Genetic algorithm in order to avoid local bests and to provide that these algorithms search in a wider area of the large search space of complex systems. In the present study, we have tested these algorithms using the migration operator in the Lotka Volterra Model. When the results are examined, it is observed that the Adaptive Genetic Algorithm with migration operator outperforms the Genetic Algorithm and the targeted success is achieved in complex system optimization. In the rest of the paper, the algorithms used in the Method Section are explained with outlines. In the Experimental Studies Section, the different algorithms are tested and compared with each other in the Lotka-Voltera model and numerical test functions. In the Conclusions and Discussions Section, brief information is given about general results and future studies.

A new random vector functional link integrated with mayfly optimization algorithm for performance prediction of solar photovoltaic thermal collector combined with electrolytic hydrogen production system

Artificial intelligence techniques have become powerful alternatives to conventional modeling techniques in different engineering disciplines. They have been applied for modeling, control, prediction, optimization, forecasting, and identification of complex systems. In this paper, a novel optimized artificial intelligence method is developed to predict the performance of Photovoltaic/Thermal Collector (PVTC) incorporated with Electrolytic Hydrogen Production (EHP) system in terms of power output of PV, PV surface cell temperature, output temperature of cooling fluid, thermal and electrical efficiency, and hydrogen production yield. A new metaheuristic algorithm called mayfly based optimization (MO) algorithm has been implemented with Random Vector Functional Link (RVFL) network to maximize the prediction accuracy. The proposed hybrid artificial intelligence model was trained and tested using experimental data. The experiments were conducted outdoors for the proposed PVTC-EHP system operating with two different cooling fluids, namely, air and water under Indian weather conditions, and their results were compared with the predicted RVFL-MO and conventional RVFL results. Moreover, five statistical criteria were used to evaluate the performance of the investigated algorithms. The experimental results showed the hybrid PVTC-EHP system can produce a daily accumulated PV output power and hydrogen production yield of 1.66 kW/day and 3.60 kg/day for water-based PVTC-EHP system and 1.22 kW/day and 4.41 kg/day for air-based PVTC-EHP system, respectively, at a mass flow rate of 0.66 kg/min. Moreover, the statistical measures showed a perfect fit between the experimental and the proposed prediction model results. The results revealed that the root mean square error for the training phase of the RVFL and RVFL-MO was 0.25 and 0.65, respectively, while it was 1.63 and 2.04 for the testing phase, which reveals the important role of MO in determining the best parameters of RVFL network that maximize its prediction performance.

Developing a Multi-objective Multi-Disciplinary Robust Design Optimization Framework

The purpose of this study is to provide an efficient Multi-Objective Multidisciplinary Robust Design Optimization (MOMRDO) framework. To this end, Bi-Level Integrated System Synthesis (BLISS) framework is implemented as a fast Multi-disciplinary Design Optimization (MDO) framework. Progressive Latin Hypercube Sampling (PLHS) is developed as a Design of Experiment (DOE) of the Uncertainty Analysis (UA). This systematic approach leads to a fast, adaptive and efficient framework for Robust Design Optimization (RDO) of complex systems. The accuracy and performance of the proposed algorithm have been evaluated with various tests. Finally, the RDO of a hydrazine monopropellant thruster is defined as a case study. The results show that the proposed method is a fast and efficient method for the multi-objective optimization design of complex systems, and this approach can be used for other engineering applications as well.

Development of a Lévy flight and FDB-based coyote optimization algorithm for global optimization and real-world ACOPF problems

This article presents an improved version of the coyote optimization algorithm (COA) that is more compatible with nature. In the proposed algorithm, fitness-distance balance (FDB) and Levy flight were used to determine the social tendency of coyote packs and to develop a more effective model imitating the birth of new coyotes. The balanced search performance, global exploration capability, and local exploitation ability of the COA algorithm were enhanced, and the premature convergence problem resolved using these two methods. The performance of the proposed Levy roulette FDB-COA (LRFDBCOA) was compared with 28 other meta-heuristic search (MHS) algorithms to verify its effectiveness on 90 benchmark test functions in different dimensions. The proposed LRFDBCOA and the COA ranked, respectively, the first and the ninth, according to nonparametric statistical results. The proposed algorithm was applied to solve the AC optimal power flow (ACOPF) problem incorporating thermal, wind, and combined solar-small hydro powered energy systems. This problem is described as a constrained, nonconvex, and complex power system optimization problem. The simulation results showed that the proposed algorithm exhibited a definite superiority over both the constrained and highly complex real-world engineering ACOPF problem and the unconstrained convex/nonconvex benchmark problems.

Efficient computation of 1D and 2D nonlinear Viscous Burgers’ equation

Model order reduction (MOR) has emerged as a de-facto solution for simulation, control, and optimization of large-scale and complex dynamical systems from the past decades. This paper demonstrates two comprehensive MOR approaches to overcome the overall computational cost required to simulate the nonlinear, viscous Burger’s equation. The former scheme is the approximate proper orthogonal decomposition (APOD), which involves capturing the approximate snapshot ensemble of the states by performing multiple linearizations along the state-trajectory. The latter technique is based on the notion of nonlinear moment matching (NLMM), in which the reduced projection basis is generated by numerically solving the simplified, nonlinear, Sylvester equation. Both the reduction schemes are tested on the high-fidelity finite difference (FD) formulation of 1D and the 2D Burgers’ equation. The results show substantial computational savings in extracting the orthonormal projection basis required to generate the reduced manifold. Furthermore, a comparative study of both methods is presented with proper orthogonal decomposition (POD) for various test signals.

Variable Parameter and Real-time Co-simulation Method and Its Application in the Analysis of Train Dynamic Air Consumption Law

System simulation technology is currently the mainstream technology for system design optimization. Parametric design can greatly improve the speed of model generation and modification by establishing various constraint relations. In this paper, the parameter design method is improved and combined with the system simulation technology, and the variable parameter and real-time co-simulation method is proposed. In order to verify the feasibility of the method in system dynamic performance analysis, this paper builds a real-time co-simulation platform based on AMESim, and applies it to the analysis process of train dynamic air consumption law. The results prove that the method is beneficial to reduce the workload of designers, and can be applied to the design optimization, analysis and tracking of complex systems.

ОПТИМИЗАЦИЯ ЦИФРОВОГО ПРОЕКТИРОВАНИЯ СЛОЖНОЙ ТЕХНОЛОГИЧЕСКОЙ СИСТЕМЫ

The article investigates the issue of optimization of a complex technical system at the stage of its design using a heuristic–phenomenological approach. The analysis of the principles of complex optimization of complex technical systems is carried out. A four–level structure for the synthesis of methodological, informational and software support for complex optimization of complex technical systems is proposed, which will allow controlling the programmable parameters of complex technical systems at each stage of their design.

Machine learning based on wave and diffusion physical systems

Recently, the application of physics to machine learning and the interdisciplinary convergence of the two have attracted wide attention. This paper focuses on exploring the internal relationship between physical systems and machine learning, and also on promoting machine learning algorithm and physical implementation. We summarize the researches of machine learning in wave systems and diffusion systems, and introduce some of the latest research results. We first discuss the realization of supervised learning for wave systems, including the wave optics realization of neural networks, the wave realization of quantum search, the recurrent neural networks based on wave systems, and the nonlinear wave computation of neural morphology. Then, we discuss the machine learning algorithms inspired by diffusion systems, such as the classification algorithm based on diffusion dynamics, data mining and information filtering based on thermal diffusion, searching for optimization based on population diffusion, etc. The physical mechanism of diffusion system can inspire the construction of efficient machine learning algorithms for the classification and optimization of complex systems and physics research, which may create a new vision for the development of physics inspired algorithms and hardware implementation, and even the integration of software and hardware.