Classical Optimization Techniques Research Articles

Generalized Distribution Feeder Switching with Fuzzy Indexing for Energy Saving

The objective of this study is to analyze feeder loss minimization and load balance under given constrains. Effective methods are required for feeder switching/reconfiguration. Feeder switching is a mixed-integer large-scale combinatorial problem for optimization, not easily solvable with classical optimization techniques, especially involving a great number of switches. This paper proposes a fuzzy indexing algorithm for feeder switching, with membership functions defined for switches such as thermometers or indices. The optimal switches can be determined through fuzzy index operations. With membership functions defined, the developed method used numerical operations for indices instead of the “set” operation or the min-max operations of traditional fuzzy algorithms. The optimization problem becomes a simple numeric calculation instead of a large-scale sorting problem and is much faster than most algorithms. It greatly reduces the computation time and enhances efficiency, which is suitable for either planning or operation purposes. Many algorithms were tested with three typical examples chosen for illustration, including the “optimal” results with an exhausted search. It shows that the proposed algorithm is very effective and can balance the load to reduce the loss and costs in obtaining the solution.

Is the online-offline buy-online-pickup-in-store retail strategy best among other product delivery strategies under variable lead time?

Every industry improves customer satisfaction by either preventing stockout situations or fulfilling the customer's demand by reducing lead time and variance of the lead time with several services offered. This study considers online selling, offline selling, and buy-online-pickup-in-store policies for selling products from retail stores. This study builds a characteristic relation between buy-online-pickup-in-store, backorder, and on-hand inventory by giving an exact total profit function instead of the expected profit function. The marginal values on lead time and variance of the lead time earn more exact outcomes for any retail strategy. The specific transportation cost for different retail strategies is considered and tested. This paper shows the analytical results which prove that the total profit of the sustainable supply chain is a convex function of both lead time and variance of the lead time. As different channel selling is crucial to any industry for increasing customer demand, the retailer sells their products by hybrid channel and advertises their products online to make them more famous. The model is solved through a classical optimization technique to get a semi-closed-form solution. In this method, it is possible to find out the optimum results for the decision variables separately by equating the first order partial derivative of the total profit with respect to the decision variables to zero and the global optimality by the Hessian matrix. A numerical experiment is conducted here, and the result shows that the supply chain system gives 14.48% more profit if advertisement policy is applied. The supply chain system gives 57.31% less profit and 72.72% less profit when only an online or offline channel is adopted, respectively. A sensitivity analysis is done for the managerial effects of cost parameters on the total profit.

Tek Modlu ve Çok Modlu Kıyaslama Fonksiyonlarını Kullanan Biyoloji Tabanlı Metasezgisel Optimizasyon Algoritmalarının Performans Karşılaştırması

Optimization is used in almost every aspect of our lives today and makes our lives easier. Optimization is generally studied as classical and heuristic optimization techniques. Classical optimization methods are not effective in real-world engineering problems. These methods, by their nature, require a mathematical model. Metaheuristic optimization methods have started to be used frequently today in the solution of these problems when a mathematical model cannot be created or a solution cannot be produced in an effective time even if it is created. These methods, by their nature, cannot produce effective results in all engineering problems. Therefore, new metaheuristic optimization methods are constantly being researched. In this study, quality test functions have been used to compare the performances of five algorithms that have been developed in recent years and produce effective results. The results obtained from these functions are shared in this study. It has been observed that the Artificial Hummingbird Optimization Algorithm (AHA) gives better results than other metaheuristic algorithms.

Surrogate-based optimization for variational quantum algorithms

Variational quantum algorithms are a class of techniques intended to be used on near-term quantum computers. The goal of these algorithms is to perform large quantum computations by breaking the problem down into a large number of shallow quantum circuits, complemented by classical optimization and feedback between each circuit execution. One path for improving the performance of these algorithms is to enhance the classical optimization technique. Given the relative ease and abundance of classical computing resources, there is ample opportunity to do so. In this work, we introduce the idea of learning surrogate models for variational circuits using few experimental measurements, and then performing parameter optimization using these models as opposed to the original data. We demonstrate this idea using a surrogate model based on kernel approximations, through which we reconstruct local patches of variational cost functions using batches of noisy quantum circuit results. Through application to the quantum approximate optimization algorithm and preparation of ground states for molecules, we demonstrate the superiority of surrogate-based optimization over commonly-used optimization techniques for variational algorithms.

Modelling and Simulation of a Redundant Agricultural Manipulator with Virtual Prototyping

The development of autonomous robots for agricultural applications includes motion planning, fruit picking, and collision avoidance with surrounding environments, and these become challenging tasks. For harvesting applications, robust control of the manipulator is needed for the effective motion of the robot. Several combinations of Proportional(P)- Integrative(I)- Derivative(D) controllers are modelled and a simulation study was performed for trajectory tracking of a redundant manipulator in virtual agricultural environments. The article presents a comprehensive study on kinematic modelling and dynamic control of redundant manipulator for fruit-picking applications in virtual environments. The collisions with surrounding environment were eliminated using ‘bounding box technique’. The joint variables are obtained by constructing Inverse Kinematics (IK) problem and are determined using a classical optimization technique. Different controllers are modelled in the ‘Simulink’ environment and are tuned to generate error-free trajectory tracking during harvesting. The task space locations (TSLs) are considered as via-points, and joint variables at each TSLs are obtained by Sequential Quadratic Programming (SQP) technique. Joint-level trajectories are generated using Quintic and B-spline polynomials. For effective trajectory tracking, torque variations are controlled using the PID and Feedforward (FF) controller. The dynamic simulations of the robot manipulator are performed in Simscape Multibody software. Results show that the during the trajectory tracking of the manipulator, the Feed-forward controller performs best with Quintic polynomial trajectory.

Leveraging Application Data Constraints to Optimize Database-Backed Web Applications

Exploiting the relationships among data is a classical query optimization technique. As persistent data is increasingly being created and maintained programmatically, prior work that infers data relationships from data statistics misses an important opportunity. We present Coco, the first tool that identifies data relationships by analyzing database-backed applications. Once identified, Coco leverages the constraints to optimize the application's physical design and query execution. Instead of developing a fixed set of predefined rewriting rules, Coco employs an enumerate-test-verify technique to automatically exploit the discovered data constraints to improve query execution. Each resulting rewrite is provably equivalent to the original query. Using 14 real-world web applications, our experiments show that Coco can discover numerous data constraints from code analysis and improve real-world application performance significantly.

A Multi-volume constraint approach to diverse form designs from topology optimization

Topology optimization methods gain extensive attention from many engineering fields for their capacities of meeting the diverse requirements of structural performances and innovative appearances. While most classical topology optimization techniques focus on globally optimal form generation around the whole design domain, there are still many demands for controlling local material proportion. This paper introduces a multi-volume constraint approach and its parameter configuration schemes to help users artificially pre-design the topologically optimized structure with the Bi-directional Evolutionary Structural Optimization (BESO) method. The numerical examples in this paper demonstrate that the presented method can be successfully applied in the computational structural form-finding for several building designs in various projects, e.g., high-rise building façades, circular shell domes, and nest-type stadium structures. It can provide the designers with diverse finely controlled structural layouts based on prescribed local material volume fractions. The structural performances of the diverse designs are very close to that of the globally optimal design. This study aims to make a bridge linking the computational optimization method to the human-centric design requirements, and it holds an enormous application potential in industrial or building designs.

Inventory models for deteriorating items with fixed lifetime, partial backordering and carbon emissions policies

In this study, a sustainable inventory model is devised to obtain the retailer’s optimal pricing and replenishment policies for degrading items having a certain lifetime incorporating partial back order as a shortage and dynamic demand under two different scenarios (a) carbon cap and trade policy (b) carbon tax policy. The primary objective of this study is to maximize the retailer’s annual total profit. The retailer’s profit function has been optimized with the help of convexity/concavity criteria employing classical optimization techniques. Based on a real case study, two different numerical examples and corresponding optimal solutions have been shown for both models with the help of Lingo 17 software. Moreover, the impact of the major inventory parameters and prominent managerial insights are presented for the robustness of the proposed model that can cooperate with industrial managers/decision-makers for the overall improvement of his/her industry to take effective and qualitative decisions.

Biodegradable waste to renewable energy conversion under a sustainable energy supply chain management.

Effective surveillance during smart cities' sustainable development allows their cleanliness to be maintained by reusing waste to produce renewable energy. This study quantifies the biodegradable waste generated in specific regions of several cities and presents ways to convert it into renewable energy. This energy can then be used to partially support regional energy demands. This study explores ways of reducing carbon emissions for biodegradable waste collection processes in regional centers, ultimately sending the biodegradable waste to the energy conversion center. The smart production system allows for the flexible production and autonomation of rates of conversion; green technology depends on each regional center's research management, which is a decision variable for reducing carbon emissions. The major contribution of this study is to consider an energy supply chain management with flexibility of energy conversion under the reduction of carbon emissions, which leads to a sustainable ESCM with the global maximum profit. This study uses mathematical modeling to decrease biodegradable waste with conversion of energy through a classical optimization technique. The solution to this mathematical model yielded significant results, providing insight into waste reduction, reduced carbon emissions and the conversion of biodegradable waste to energy. The model is examined using numerical experiments, and its conclusion supports the model with the fundamental assumptions. Results of sensitivity analysis provide insight into the reduction and re-utilization of wastes, carbon emission reduction, and the benefits of using renewable energy.

Finite-Time Consensus Dual Algorithm for Distributed Optimization over Digraphs

In this paper, we solve the convex distributed optimization problems, which include unconstrained optimization and a special constrained problem commonly known as a resource allocation problem, over a network of agents among which the communication can be represented by directed graphs (digraphs), by using the finite-time consensus-based and dual-based first-order gradient descent (GD) techniques. The key point is that a special consensus matrix is utilized for problem reformulation to make our dual-based algorithm suitable for digraphs. By the property of distributed finite-time exact (not approximate) consensus, the classical centralized optimization techniques (e.g., Nesterov accelerated GD) can be embedded into our dual-based algorithm conveniently, which means our distributed algorithm can inherit performance of classical centralized algorithms that has been proved to have optimal convergence performance. As a result, our proposed algorithm has faster convergence rate related to the optimization iteration number compared with other distributed optimization algorithms in literature. Since there are finite consensus communication steps inside each consensus process, when the time needed to communicate values between two neighbors is less than a threshold of the time needed to perform local computations, our proposed algorithm is also faster related to the time, as demonstrated in the simulations.

An efficient sustainable smart approach to biofuel production with emphasizing the environmental and energy aspects

By using renewable biomass, the advancement in biofuel production through a smart inspection production framework is a crucial substitute to fossil fuels which support minimizing environmental pollution and zero carbon emissions. Traditional biofuel production can be converted into a sustainable smart production system by utilizing smart machines, skilled labor, and reduced energy consumption. The prime purpose of this study is to produce pure biofuel with less energy consumption and carbon emissions through a smart production framework of biofuel. The work-in-process inventory is calculated for different costs, including air handling, lighting, and carbon emissions, in each stage of the study. Although smart variable production is utilized, still impure biofuel is produced with a random production rate. The impure biofuel is purified to make it pure. A multi-delivery technique is used such that a fixed amount of biofuel of n segments of the pure biofuel is transported to the market as per the market’s demand. The total profit of this study is maximized globally with the help of the classical optimization technique. Four numerical examples are observed to validate the model. The applicability and efficiency of the proposed model are finally demonstrated through a real case study in India. Two special cases prove the necessity of smart production and discrete investment for setup cost reduction. The sensitivity analysis and the graphical representation give each parameter’s effect on the model’s total profit. Numerical results prove that if no discrete investment for setup cost reduction and fixed production rate is taken, the common production cycle time is increased remarkably. It is seen that the total profit of this study rises 4.30% from the traditional production system. Moreover, numerical results prove a major effect on renewable energy in a sustainable smart production system.

Advertisement policy for dual-channel within emissions-controlled flexible production system

Nowadays, industrial firms are very much careful to build a green environment by reducing carbon emissions. The government imposes some rules and regulations to provide a better eco-friendly environment. In this study, the cap-and-trade mechanism has been considered in a production model to control the carbon emissions rate. The manufacturers invest in advanced green technology to reduce per unit emissions. As online and offline selling is crucial to any industry for increasing customer demand, the manufacturers sell their products by dual-channel and advertise their products by online channel to make more popular of their products. Keeping these in mind, a sustainable flexible production model with single-type substitutable product production is considered here by imposing a cap-and-tax policy, investing in green technology, and advertising for products. This model is divided into two cases: with and without investment in green technology. The demand of each manufacturer depends upon an online selling price, an offline selling price, and an online advertisement of the product. A classical optimization technique helps to get the optimum strategies for the online selling price, offline selling price, advertisement investment, green technology, cycle time, and production rate. From the numerical study, it is proved that the industry gets 6.97% more profit in the case of green technology investment and the proposed study gives 5.74% more profit than the traditional production system. Sensitivity analysis and managerial insights are performed.

Cocktail parity problem solution based on modified blind extraction technique

The Cocktail Party Problem solution is described as being the responsibility of isolating the voice signal in a noisy environment. Two popular methods for resolving this issue are blind source extraction and the Wiener filtering procedure. The blind source extraction approaches like fastICA, JADE and efficient fastICA are useful for extraction data from the mixed signals. The classical optimization techniques such as genetic algorithms or particle swarms for blind source extraction are mostly founded on the gradient and need the objective function, so the using of these techniques is very restricted. In the recent studies, the classical blind source separation techniques will not give the perfect solution for the cocktail parity problem. These methods struggle with convergence speed and accuracy issues as well. In order to enhance the separation process and get over these issues, this work adopted the glowworm swarm technique based on kurtosis as the objective function. The results show that the proposed technique produces good separation. The original and estimated signals are compared for similarity using the cross-correlation function.

Deep Learning for Multi-User MIMO Systems: Joint Design of Pilot, Limited Feedback, and Precoding

In conventional multi-user multiple-input multiple-output (MU-MIMO) systems with frequency division duplexing (FDD), channel acquisition and precoder optimization processes have been designed separately although they are highly coupled. This paper studies an end-to-end design of downlink MU-MIMO systems which include pilot sequences, limited feedback, and precoding. To address this problem, we propose a novel deep learning (DL) framework which jointly optimizes the feedback information generation at users and the precoder design at a base station (BS). Each procedure in the MU-MIMO systems is replaced by intelligently designed multiple deep neural networks (DNN) units. At the BS, a neural network generates pilot sequences and helps the users obtain accurate channel state information. At each user, the channel feedback operation is carried out in a distributed manner by an individual user DNN. Then, another BS DNN collects feedback information from the users and determines the MIMO precoding matrices. A joint training algorithm is proposed to optimize all DNN units in an end-to-end manner. In addition, a training strategy which can avoid retraining for different network sizes for a scalable design is proposed. Numerical results demonstrate the effectiveness of the proposed DL framework compared to classical optimization techniques and other conventional DNN schemes.

Magnetic particle tracking: A semi-algebraic solution

Magnetic Particle Tracking (MPT) is a relatively new non-invasive measurement technique which is often used to study dense granular flow. Its basic principle relies on tracking the movement of a single magnetic tracer by means of measuring the magnetic field strength at a suitable distance from the tracer. By assumption of a magnetic dipole and the use of minimization techniques, both location and orientation of the tracer can be determined. MPT is therefore uniquely suited for the study of non-spherical particles. The performance of the localization is largely dependent on the signal-to-noise ratio and very often relies on nonlinear optimization techniques, as the definition of the magnetic field generated by a dipole is highly nonlinear and has five degrees of freedom. In this paper, we present a semi-algebraic solution by decoupling the estimation of the position and orientation in separate algebraic solutions. The two estimates are mutually dependent, necessitating an iterative approach between the two. The main benefits of this new approach is in the speed and robustness of the algorithm, which are much higher than for the classical constrained nonlinear optimization techniques.

Quantum optimization of complex systems with a quantum annealer

We perform an in-depth comparison of quantum annealing with several classical optimisation techniques, namely thermal annealing, Nelder-Mead, and gradient descent. We begin with a direct study of the 2D Ising model on a quantum annealer, and compare its properties directly with those of the thermal 2D Ising model. These properties include an Ising-like phase transition that can be induced by either a change in 'quantum-ness' of the theory, or by a scaling the Ising couplings up or down. This behaviour is in accord with what is expected from the physical understanding of the quantum system. We then go on to demonstrate the efficacy of the quantum annealer at minimising several increasingly hard two dimensional potentials. For all the potentials we find the general behaviour that Nelder-Mead and gradient descent methods are very susceptible to becoming trapped in false minima, while the thermal anneal method is somewhat better at discovering the true minimum. However, and despite current limitations on its size, the quantum annealer performs a minimisation very markedly better than any of these classical techniques. A quantum anneal can be designed so that the system almost never gets trapped in a false minimum, and rapidly and successfully minimises the potentials.

Efficient estimation of tuned liquid column damper inerter (TLCDI) parameters for seismic control of base‐isolated structures

AbstractThis paper presents an enhanced base‐isolation (BI) system equipped with a novel passive control device composed of a tuned liquid damper and an inerter (TLCDI). With the aim of reducing the seismic response of BI systems, this contribution focuses on the design of the TLCDI providing analytical solutions for the optimal TLCDI parameters, easily implementable in the design phase. The effectiveness of the proposed approach in terms of seismic response reduction and computational gain is validated by comparison with classical numerical optimization techniques. The control performance of two different base‐isolated TLCDI‐controlled structures is assessed by employing real‐ground motion records, and relevant comparisons with both uncontrolled base‐isolated structures and equipped with a conventional TLCD are presented.

Introduction of a novel evolutionary neural network for evaluating the compressive strength of concretes: A case of Rice Husk Ash concrete

The construction industry is facing challenges from the hazardous nature of Ordinary Portland Cement (OPC) production as one of the main contributors to global warming and CO2 emission. Given its increasing demand, the need to replace OPC with sustainable alternatives for green concrete production is essential. The promising results of using Rice Husk Ash (RHA) in concrete mixtures as a supplementary cementitious material (SCM) have attracted great attention. However, conventional laboratory procedures for the analysis of concrete properties like Compressive Strength (CS) are laborious and expensive. Therefore, developing a reliable and accurate model for its prediction can save time and reduce operational costs. In this study, the Artificial Neural Network (ANN) technique was hybridized with a robust optimization technique called Particle Swarm Optimization algorithm with Two Differential mutations (PSOTD) to predict the CS of RHA concrete. To compare the results of the PSOTD over classical optimization techniques, three other algorithms, including the best reported conventional Back-Propagation algorithm (i.e., Levenberg-Marquardt (LM)), Differential Evolution (DE), and Particle Swarm Optimization (PSO), were employed. For the training dataset, the coefficients of determination obtained for the ANN-LM, ANN-DE, ANN-PSO, and ANN-PSOTD were 0.9797, 0.9389, 0.9462, and 0.9803, while for the testing dataset, the values were 0.9682, 0.9198, 0.9223, and 0.9697, respectively. Overall, the error measures show the superiority of the ANN-PSOTD in predicting the CS of RHA concretes. To further enhance the prediction accuracy and reliability, the hybrid model of ANN-LM and ANN-PSOTD (i.e., ANN-PSOTD-LM) was also proposed. However, there is a trade-off between the computational time and the accuracy of models when the ANN-PSOTD-LM technique is chosen.

Transit facility allocation: Hybrid quantum-classical optimization.

An essential consideration in urban transit facility planning is service efficiency and accessibility. Previous research has shown that reducing the number of facilities along a route may increase efficiency but decrease accessibility. Striking a balance between these two is a critical consideration in transit planning. Transit facility consolidation is a cost-effective way to improve the quality of service by strategically determining the desirable allocation of a limited number of facilities. This paper develops an optimization framework that integrates Geographical Information systems (GIS), decision-making analysis, and quantum technologies for addressing the problem of facility consolidation. Our proposed framework includes a novel mathematical model that captures non-linear interactions between facilities and surrounding demand nodes, inter-facility competition, ridership demand and spatial coverage. The developed model can harness the power of quantum effects such as superposition and quantum tunnelling and enables transportation planners to utilize the most recent hardware solutions such as quantum and digital annealers, coherent Ising Machines and gate-based universal quantum computers. This study presents a real-world application of the framework to the public transit facility redundancy problem in the British Columbia Vancouver metropolitan area. We demonstrate the effectiveness of our framework by reducing the number of facilities by 40% while maintaining the same service accessibility. Additionally, we showcase the ability of the proposed mathematical model to take advantage of quantum annealing and classical optimization techniques.

A smart production system with an autonomation technology and dual channel retailing

Smart products can be sold through online or offline with different price range. For this, the production management has to take care about the price in both channel. A smart single-stage production model with an autonomation inspection technology is considered to develop this prevailing study. Different quality products are sold in different channels with different price (i.e., online and offline). Due to different quality and channel, the price is also varies based on quality or channel. Owing to this situation, the total demand depends on the price of a different quality product. The imperfect products are produced in the out-of-control situation, which causes the backordered. The generation of defective items lead to generation of wastes, and wastes generation create a bad impact for the industry both economically and environmentally. The imperfect or defective items are detected through a smart autonomation inspection technology and sent back for remanufacturing in the same production cycle. The decision-making variables are obtained through a classical optimization technique. A lemma has been proved to finalize the global optimum solution of the decision-making variables. Numerical experiments prove that the manufacturing system provides 0.3% better result, when the defective rate follows a Triangular distribution, whereas the production increases the profit up to 35% due to use of online-to-offline retailing system, and reduces waste up to 50.84% due to use of autonomation inspection. Moreover, the impact of critical parameters on the total system profit is presented through the sensitivity analysis and graphical representations.