Optimization Algorithm For Design Research Articles

A data-driven discrete simulation-based optimization algorithm for car-sharing service design

This paper formulates a discrete simulation-based optimization (SO) algorithm for a family of large-scale car-sharing service design problems. We focus on the profit-optimal assignment of vehicle fleet across a network of two-way (i.e., round-trip) car-sharing stations. The proposed approach is a metamodel SO approach. A novel metamodel based on a mixed-integer program (MIP) is formulated. The metamodel is embedded within a general-purpose discrete SO algorithm. The proposed algorithm is validated with synthetic toy network experiments. The algorithm is then applied to a high-dimensional Boston case study using reservation data from a major US car-sharing operator. The method is benchmarked versus several algorithms, including stochastic programming. The experiments indicate that the analytical network model information, provided by the MIP to the SO algorithm, is useful both at the first iteration of the algorithm and across subsequent iterations. The solutions derived by the proposed method are benchmarked versus the solution deployed in the field by the car-sharing operator. Via simulation, the proposed solutions improve those deployed with an average improvement of profit of 6% and of vehicle utilization of 3%.The combination of the problem-specific analytical MIP with a general-purpose SO algorithm enables the discrete SO algorithm to: (i) address high-dimensional problems, (ii) become computationally efficient (i.e., it can identify good quality solutions within few simulation observations), (iii) become robust to the quality of the initial points and of the stochasticity of the simulator. More generally, the information provided by the MIP to the SO algorithm enables it to exploit problem-specific structural information. This leads to an algorithm with both asymptotic convergence guarantees as well as good short term performance (i.e., performance given few simulation observations). We view this general idea of combining analytical MIP formulations with general-purpose SO algorithms, or more broadly with general-purpose sampling strategies of high-resolution data, as an innovative and promising area of future research.

The Structural Design and Optimization of Top-Stiffened Double-Layer Steel Truss Bridges Based on the Response Surface Method and Particle Swarm Optimization

A lightweight design optimization algorithm is proposed to optimize the design parameters of stiffened double-layer steel girder bridges, the aim of which is to improve structural safety and reduce superstructure works. Taking a top-stiffened double-layer steel truss bridge as the reference project, a multiscale mixed-element model of the initial design parameters is established, and its computational accuracy is verified. Considering the structural configuration and loading characteristics of the bridge, the elastic modulus of steel, the deck plate thickness, the stiffening vertical bar height, and the relative distance between the double-layer main girders are selected as the design parameters for optimization. The mid-span vertical deflection, the axial forces in the stiffeners, the bottom plate of the deck, the compressed web tube at the pier top, and the quantity of superstructure works are chosen as the objective functions to be minimized. A lightweight calculation equation reflecting the relationship between the optimization parameters and the objective functions is established using the response surface method (RSM). Subsequently, an improved weighted particle swarm optimization (WPSO) model is employed to perform the multi-objective optimization of the design parameters for the bridge, and the results are compared with those obtained from the multi-objective genetic algorithm NSGA-II. The results show that the RSM accurately fits the numerical relationship between the optimization parameters and the objective response functions. When minimizing the quantity of superstructure works as the primary control objective and minimizing the mid-span vertical deflection and the axial forces in the compressed web tube at the pier top as secondary control objectives, the optimization results achieved by WPSO outperform those obtained by NSGA-II. The optimized results lead to reductions of 11.09%, 3.92%, 7.56%, 4.45%, and 8.38% in the respective objective function values of the structure. This method has important theoretical significance for the optimization of structural design parameters.

A Kriging-based decoupled non-probability reliability-based design optimization scheme for piezoelectric PID control systems

When dealing with optimization problems, the introduction of uncertainty will greatly increase the difficulty of solving the problem. The traditional reliability-based design optimization (RBDO) algorithm coupling uncertainty parameters and design variables, and solve optimization with optimization algorithms. The traditional RBDO scheme is easy to understand and realize, but it will greatly increase the calculation time, and it often does not get ideal results when solving some problems with high time complexity. The Kriging-based decoupled non-probability reliability-based design optimization (K-BRBDO) scheme proposed in this paper decouples uncertainty parameters and design variables, establishes a decoupled optimization system based on Kriging model, and proposes a new adaptive learning strategy which involves two stages of enrichment to improve the accuracy of the surrogate model in the region of interest. Finally, the effectiveness and accuracy of the optimization scheme are verified by two numerical examples and an engineering example.

Machine learning algorithms in ship design optimization

ABSTRACT Numerical optimization of complex systems benefits from the technological development of computing platforms in the last 20 years. Unfortunately, this is still not enough, and a large computational time is necessary for the solution of optimization problems when mathematical models that implement rich (and therefore realistic) physical models are adopted. In this paper, we show how the combination of optimization and Artificial Intelligence (AI), in particular Machine Learning algorithms, can help, strongly reducing the overall computational times, making also possible the use of complex simulation systems within the optimization cycle. Original approaches are proposed.

Optimal design and performance analysis of coastal microgrid using different optimization algorithms

AbstractOwing to the stochastic behavior of renewable energy activity and the multiple design considerations, the advancement of hybrid renewable energy-based microgrid (HREMG) systems has become a complex task. This study proposes a design optimization algorithm for the long-term operation of an autonomous HREMG along with the optimal system capacities. The investigated energy system comprises photovoltaic panels, wind turbines, diesel generators, and batteries. It aims to energize a remote coastal community with a daily load demand of 400 kWh in Marsa Matruh, Egypt. Since most studies utilize commercial tools in the design optimization procedure, the African vultures optimization approach (AVOA) is developed to find the optimal energy alternative and determine the optimal component’s capacity considering achieving the minimum energy cost and loss of power supply probability. Moreover, an adequate energy management strategy is suggested to coordinate the power flow within the energy system in which renewable energy sources are fully penetrated. To check the AVOA robustness and efficacy, its performance is compared with the HOMER Pro most popular commercial tool as well as with new metaheuristic algorithms, namely the grasshopper optimization algorithm (GOA) and Giza pyramid construction (GPC) under the same operating environment. The results revealed that the proposed AVOA achieved superior economic results toward the least net present cost ($346,614) and energy price (0.0947 $/kWh). Moreover, over 20 independent runs, the AVOA showed a better performance in terms of convergence and execution time compared to other tools/algorithms. The obtained findings could be a useful benchmark for researchers in the sizing problem of hybrid energy systems.

Performance evaluation of a non linear PID controller using chaotic gravitational search algorithm for a twin rotor system

AbstractA novel strategy using a chaotic gravitational search algorithm (CGSA) based nonlinear PID control scheme, which is validated through a laboratory helicopter model called the twin rotor system, is presented in this paper. In this work, CGSA is used as a stochastic based global optimization algorithm for controller design in the twin rotor system adopted. The fine chaotic search process used in CGSA obtains the optimal solution in the iterative process based on the current best solution. The goal of the controller design in this paper is to stabilize the twin rotor system with considerable cross couplings to reach the selected position and follow the desired trajectory effectively. The addition of nonlinear functions to the PID controller structure initiates better error tracking and facilitates smooth output under changing input conditions. The design objective is to implement a nonlinear PID control scheme for the angular displacements of the twin rotor system with minimization of the integral square error (ISE) as the fitness function in the algorithm. The statistical performance of the controller is analyzed by considering the best, worst, mean, and standard deviations of ISE. In this work, simultaneous control of pitch and yaw angles is considered to get rid of the coupling effect between the two rotors. From the simulation results it is observed that the proposed work shows better performance than the other evolutionary computation techniques. The results also indicate the advantage of the proposed CGSA based tuning for the two degree of freedom MIMO control with standard reference trajectories as per the TRMS330‐10 model.

Design framework for rehabilitation grout materials during metro operation period: A combination of improved central composite design modeling and multi-objective particle swarm optimization

Grouting is one of the most effective measures for the rehabilitation of metro tunnels during the operation period. However, a paradox involving fast stiffening, high fluidity and reasonable setting time for grout materials might be encountered. For this achievement, a design framework for the operation metro grout materials based on improved central composite design (CCD) and multi-objective particle swarm optimization algorithm (MOPSO). Sulphoaluminate cement (SAC) is regarded as basis materials, mix proportion parameters are considered as decision variables, early strength, fluidity, and initial setting time are taken as objectives. Stepwise regression method is adopted to modify the classical method for indicators modeling. With the fitness functions by stepwise regression method, MOPSO is performed for multi-objective optimization. Finally, an optimal solution is determined by Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results are as follows: (1) Stepwise regression method improved quadratic models could reduce complexity and increase significance. (2) The determination coefficients of prediction models for early strength, fluidity, and initial setting time are 0.918, 0.948, and 0.907, respectively. (3) Pareto set is obtained with the maximization of the objective. (4) The effect of weight distribution on the objectives is analyzed. The proposed design framework can be useful and practical for multiple decision making where engineers face grout design.

Artificial Eco-System-Based Optimization Algorithm for Optimal Design of Single-Machine Infinite Bus and Multi-Machine Power System Stabilizers

Artificial Eco-System-Based Optimization Algorithm for Optimal Design of Single-Machine Infinite Bus and Multi-Machine Power System Stabilizers

Application of state-of-the-art multiobjective metaheuristic algorithms in reliability-based design optimization: a comparative study

Application of state-of-the-art multiobjective metaheuristic algorithms in reliability-based design optimization: a comparative study

Single and Multiple Gate Design Optimization Algorithm for Improving the Effectiveness of Fiber Reinforcement in the Thermoplastic Injection Molding Process.

Fiber reinforcement orientation in thermoplastic injection-molded components is both a strength as well as a weak point of this largely employed manufacturing process. Optimizing the fiber orientation distribution (FOD) considering the shape of the part and the applied loading conditions allows for enhancing the mechanical performances of the produced parts. Henceforth, this research proposes an algorithm to identify the best injection gate (IG) location/s starting from a 3D model and a user-defined load case. The procedure is composed of a first Visual Basic Architecture (VBA) code that automatically sets and runs Finite Volume Method (FVM) simulations to find the correlation between the fiber orientation tensor (FOT) and the IG locations considering single and multiple gates combinations up to three points. A second VBA code elaborates the results and builds a dataset considering the user-defined loading and constraint conditions, allowing the assignment of a score to each IG solution. Three geometrical components of increasing complexity were considered for a total of 1080 FVM simulations and a total computational time of ~390 h. The search for the best IG location has been further expanded by training a Machine Learning (ML) model based on the Gradient Boosting (GB) algorithm. The training database (DB) is based on FVM simulations and was expanded until a satisfactory prediction accuracy higher than 90% was achieved. The enhancement of the local FOD on the critical regions of three components was verified and showed an average improvement of 26.9% in the stiffness granted by a high directionality of the fibers along the load path. Finite element method (FEM) simulations and laboratory experiments on an industrial pump housing, injection-molded with a polyamide-66 reinforced with 30% of short glass fibers (PA66-30GF) material were also carried out to validate the FVM-FEM simulation frame and showed a 16.4% local stiffness improvement in comparison to the currently employed IG solution.

Development of a design optimization algorithm for a bowtie antenna

A bowtie antenna is one of the simple dipole antennas with an omnidirectional pattern utilized in several applications. It is used in industrial applications, scientific applications, and medical applications. Its elementary design can be subjected to modification to expand the applications of the dipole and improve its performance. This paper aims to develop a design optimization algorithm for a bowtie antenna with an adaptive finite impulse response (FIR) filter. In the paper, the different designs of the bowtie antenna are simulated using MATLAB software. The design of antennas is constructed using the partial differential equation (PDE) toolbox in MATLAB software. The designs explored in the paper are the slotted microstrip bowtie antenna and the double flare bowtie antenna. A traditional bowtie was also simulated to be used as a reference for the evaluation of the modified antennas. The dimensions of the designs are kept closely like draw accurate conclusions about the effects of the refinements done. The effects of the modification of the designs on the directivity and return loss are determined to assess the effectiveness of the design alterations.

FCM with Spatial Constraint Multi-Kernel Distance-Based Segmentation and Optimized Deep Learning for Flood Detection

Floods are the deadly and catastrophic disasters, causing loss of life and harm to assets, farmland, and infrastructure. To address this, it is necessary to devise and employ an effective flood management system that can immediately identify flood areas to initiate relief measures as soon as possible. Therefore, this research work develops an effective flood detection method, named Anti- Corona-Shuffled Shepherd Optimization Algorithm-based Deep Quantum Neural Network (ACSSOA-based Deep QNN) for identifying the flooded areas. Here, the segmentation process is performed using Fuzzy C-Means with Spatial Constraint Multi-Kernel Distance (MKFCM_S) wherein the Fuzzy C-Means (FCM) is modified with Spatial Constraints Based on Kernel-Induced Distance (KFCM_S). For flood detection, Deep QNN has been used wherein the training progression of Deep QNN is done using designed optimization algorithm, called ACSSOA. Besides, the designed ACSSOA is newly formed by the hybridization of Anti Corona Virus Optimization (ACVO) and Shuffled Shepherd Optimization Algorithm (SSOA). The devised method was evaluated using the Kerala Floods database, and it acquires the segmentation accuracy, testing accuracy, sensitivity, and specificity with highest values of 0.904, 0.914, 0.927, and 0.920, respectively.

Cheetah optimization algorithm for optimum design of heat exchangers

Abstract Thermal system optimization is always a challenging task due to several constraints and critical concepts of thermo-hydraulic aspects. Heat exchangers are one of those devices that are widely adopted in thermal industries for various applications such as cryogenics, heat recovery, and heat transfer applications. According to the flow configurations and enhancement of fins, the heat exchangers are classified as plate-fin heat exchangers, shell and tube heat exchangers, and tube-fin heat exchangers. This article addresses the economic optimization challenge of plate-fin heat exchangers using cheetah optimization (CO) algorithm. The design variables were optimized using the CO algorithm, and statistical results were compared with eight well-established algorithms. The study revealed that the cheetah algorithm is prominent in terms of realizing minimizing the overall cost of the plate-fin heat exchanger with a 100 % of success rate. Furthermore, the study suggests adopting the cheetah optimizer for solving optimization challenges in different fields.

Proportional Topology Optimization algorithm for two-scale concurrent design of lattice structures

In this paper, the Proportional Topology Optimization (PTO) algorithm is extended for the two-scale concurrent topology optimization, in which both the structure and material cellular micro-structure are subject to design. PTO was originally developed on the concept that the amount of material being distributed to an element would be proportional to the contribution of that element in the objective function. Sensitivity analysis is not required. In a two-scale concurrent topology optimization problem, two sets of design variables are defined, one for macro-structure and one for micro-structure. Here, the objective function is reformulated such that the contribution of each micro-scale design variable can be determined, facilitating the employment of PTO. The macroscopic effective elastic tensor is evaluated by the energy-based homogenization method (EBHM), providing a link between micro-structure and macro-structure. Feasibility and efficiency of the proposed PTO approach are demonstrated via several benchmark examples of both two and three dimensional structures.

Artificial Intelligence Aided Design of Hull Form of Unmanned Underwater Vehicles for Minimization of Energy Consumption

Abstract Designing an excellent hull to reduce the sailing path energy consumption of UUVs is crucial for improving the energy endurance of UUVs. However, path energy consumption-based UUV hull design requires a tremendous amount of calculation due to the frequent changes in relative velocity and attack angle between a UUV and ocean current. In order to address this issue, this work developed a data-driven design methodology for energy consumption-based UUV hull design using artificial intelligence-aided design (AIAD). The design methodology in this work combined a deep learning (DL) algorithm that predicts UUVs’ resistance with different hull shapes under different velocities and attack angles with the particle swarm optimization (PSO) algorithm for UUV hull design. We tested the proposed methodology in a path energy consumption-based experiment, where the optimized UUV hull showed an 8.8% reduction in path energy consumption compared with the initial UUV hull, and design costs were greatly reduced compared with the traditional computational fluid dynamics (CFD)-based methodology. Our work demonstrates that AIAD has the potential to solve UUV design problems previously thought to be too complex by offering a data-driven engineering shape (body surface) design method.

MM optimization: Proximal distance algorithms, path following, and trust regions

We briefly review the majorization-minimization (MM) principle and elaborate on the closely related notion of proximal distance algorithms, a generic approach for solving constrained optimization problems via quadratic penalties. We illustrate how the MM and proximal distance principles apply to a variety of problems from statistics, finance, and nonlinear optimization. Drawing from our selected examples, we also sketch a few ideas pertinent to the acceleration of MM algorithms: a) structuring updates around efficient matrix decompositions, b) path following in proximal distance iteration, and c) cubic majorization and its connections to trust region methods. These ideas are put to the test on several numerical examples, but for the sake of brevity, we omit detailed comparisons to competing methods. The current article, which is a mix of review and current contributions, celebrates the MM principle as a powerful framework for designing optimization algorithms and reinterpreting existing ones.

Application of Modified Hybrid Vision Correction Algorithm for Water Distribution Systems in Civil Engineering

Water distribution systems (WDSs) are critical for providing societal convenience and welfare. However, designing an efficient WDS can be challenging owing to the need to satisfy various parameters and hydraulic conditions, such as physical parameters like pipe diameter and length, and minimum required water pressure at each node. To tackle these constraints, several studies have utilized metaheuristic optimization algorithms for the optimal design of WDSs. In this study, we used a modified hybrid vision correction algorithm (MHVCA) to determine the optimal designs of different WDSs. To assess the performance of MHVCA prior to its application in optimizing the WDS design, its effectiveness was evaluated by applying it to a mathematical problem. Subsequently, the optimal design of the WDS was determined by employing a MHVCA, and its performance was verified. The results demonstrate that MHVCA achieved a maximum of 1.5% lower average cost than the other algorithms as the pipe network complexity increased. These results suggest that the optimal design results obtained using the MHVCA can be applied to other WDS problems not considered in this study.

A Unified Approach to Shape and Topological Sensitivity Analysis of Discretized Optimal Design Problems

We introduce a unified sensitivity concept for shape and topological perturbations and perform the sensitivity analysis for a discretized PDE-constrained design optimization problem in two space dimensions. We assume that the design is represented by a piecewise linear and globally continuous level set function on a fixed finite element mesh and relate perturbations of the level set function to perturbations of the shape or topology of the corresponding design. We illustrate the sensitivity analysis for a problem that is constrained by a reaction–diffusion equation and draw connections between our discrete sensitivities and the well-established continuous concepts of shape and topological derivatives. Finally, we verify our sensitivities and illustrate their application in a level-set-based design optimization algorithm where no distinction between shape and topological updates has to be made.

A solar irradiance estimation technique via curve fitting based on dual-mode Jaya optimization

Solar irradiance is a crucial environmental parameter for optimal control of photovoltaic (PV) systems. However, precise measurements of the solar irradiance are difficult since the irradiation sensors (i.e., pyranometer or pyrheliometer) are expensive and hard to calibrate. This paper proposes a cost-effective and accurate method for estimating the solar irradiance with a PV module via curve fitting. A dual-mode Jaya (DM-Jaya) optimization algorithm is introduced to extract the real-time value of solar irradiance from the measured PV characteristics data by using two search strategies. The step sizes of a random walk are taken from even and Lévy distribution distributions in different searching phases. Compared with the traditional irradiance sensors, the proposed estimator does not require additional circuit and obtains relatively lower error rates. A comparative study of seven population-based optimization algorithms for the optimal design of the estimator is presented. These algorithms include particle swarm optimization (PSO), cuckoo search (CS), Jaya, simulated annealing (SA), genetic algorithm (GA), supply-demand-based optimization (SDO), and the proposed DM-Jaya algorithm. Simulations and experimental results reveal that DM-Jaya outperforms the other optimization algorithms in terms of the estimation speed and accuracy.

Nonlinear dynamic buckling and multi-objective design optimisation of FG-GPLRP plates

Functionally graded graphene platelets reinforced porous (FG-GPLRP) composites have the advantages of tuneable properties, lightness, and enhanced mechanical capacity, which inspire the customised and intelligent multi-objective optimal structural designs. This study explores the nonlinear buckling response and resolves the multi-objective design optimisation of FG-GPLRP plates subjected to various biaxial impacts for the first time. Manifold practical influences on nonlinear dynamic stability and impulse-carrying capacity have been incorporated and investigated meticulously. By improving the state-of-the-art technique, the Competitive and Adaptive Learning Multi-objective Poplar Optimisation Algorithm (CALMOPOA) has been innovatively proposed for structural design optimisation. Through the developed framework, the multi-objective optimal designs of the FG-GPLRP structure possessing maximum nonlinear dynamic stability, impulse resistance ability, and contradictory minimum composite mass, have been resolved in the form of the Pareto front. In numerical studies, the CALMOPOA is compared against competitive and prevalent existing design optimisation algorithms, reflecting vast merits in superior optimal composite designs, more advantageous convergence rates and diversity characteristics. Moreover, the modularised feature of the presented structural design optimisation facilitates user-friendliness and practicality in realistic engineering applications. The proposed study can enable the demand-orientated customised, intellectualised, and modularised composite designs for the trending strategic blueprints.