An Effective Mixed Annealing/Heuristic Algorithm for Problems in Mechanical Design

1995 ◽  
Vol 117 (3) ◽  
pp. 409-418 ◽  
Author(s):  
M. M. Ogot ◽  
S. S. Alag
Keyword(s):  
Stochastic Optimization ◽  
Heuristic Algorithm ◽  
Design Space ◽  
Simulated Annealing Algorithm ◽  
Mechanical Design ◽  
Optimization Technique ◽  
Computation Time ◽  
Optimization Methods ◽  
Computational Effort ◽  
Stochastic Algorithm

The wide application of stochastic optimization methods in mechanical design has been partially hindered due to (a) the relatively long computation time required, and (b) discretization of the design space at the onset of the optimization process. This work proposes a new stochastic algorithm, the Mixed Annealing/Heuristic Algorithm (MAH), which addresses both these issues. It is based on the Simulated Annealing algorithm (SA) and the Heuristic Optimization Technique (HOT). Both these algorithms have been successfully applied to problems in mechanical design and up to now have been considered as competing algorithms. MAH capitalizes on each of their individual strengths and addresses their weaknesses, thereby considerably reducing the computational effort required to attain the final solution. A pseudo-continuous approach for configuration generation is employed, making the discretization of the design space no longer necessary. The effectiveness of MAH is demonstrated via three problems in kinematic synthesis. Comparison of the results with other stochastic optimization methods illustrates the potential of this technique.

NON-LINEAR GLOBAL OPTIMIZATION VIA PARAMETERIZATION AND INVERSE FUNCTION APPROXIMATION: AN ARTIFICIAL NEURAL NETWORKS APPROACH

2007 ◽  
Vol 17 (05) ◽  
pp. 353-368 ◽  
Author(s):  
RENÉ V. MAYORGA ◽  
MARIANO ARRIAGA
Keyword(s):  
Global Optimization ◽  
Optimization Technique ◽  
Inverse Function ◽  
Optimization Methods ◽  
Computational Effort ◽  
Linear Functions ◽  
Local Minima ◽  
Non Linear ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this article, a novel technique for non-linear global optimization is presented. The main goal is to find the optimal global solution of non-linear problems avoiding sub-optimal local solutions or inflection points. The proposed technique is based on a two steps concept: properly keep decreasing the value of the objective function, and calculating the corresponding independent variables by approximating its inverse function. The decreasing process can continue even after reaching local minima and, in general, the algorithm stops when converging to solutions near the global minimum. The implementation of the proposed technique by conventional numerical methods may require a considerable computational effort on the approximation of the inverse function. Thus, here a novel Artificial Neural Network (ANN) approach is implemented to reduce the computational requirements of the proposed optimization technique. This approach is successfully tested on some highly non-linear functions possessing several local minima. The results obtained demonstrate that the proposed approach compares favorably over some current conventional numerical (Matlab functions) methods, and other non-conventional (Evolutionary Algorithms, Simulated Annealing) optimization methods.

scholarly journals Optimal Placement of Distributed Photovoltaic Systems and Electric Vehicle Charging Stations Using Metaheuristic Optimization Techniques

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2378
Author(s):  
Kulsomsup Yenchamchalit ◽  
Yuttana Kongjeen ◽  
Prakasit Prabpal ◽  
Krischonme Bhumkittipich
Keyword(s):  
Electric Vehicle ◽  
Simulated Annealing Algorithm ◽  
Optimization Technique ◽  
Computation Time ◽  
Optimization Techniques ◽  
Optimal Placement ◽  
Total Power ◽  
Optimal Conditions ◽  
Optimal Position ◽  
Charging Stations

In this study, the concept of symmetry is introduced by finding the optimal state of a power system. An electric vehicle type load is present, where the supply stores’ electrical energy causes an imbalance in the system. The optimal conditions are related by adjusting the voltage of the bus location. The key variables are the load voltage deviation (LVD), the variation of the load and the power, and the sizing of the distributed photovoltaic (DPV), which are added to the system for power stability. Here, a method to optimize the fast-charging stations (FCSs) and DPV is presented using an optimization technique comparison. The system tests the distribution line according to the bus grouping in the IEEE 33 bus system. This research presents a hypothesis to solve the problem of the voltage level in the system using metaheuristic algorithms: the cuckoo search algorithm (CSA), genetic algorithm (GA), and simulated annealing algorithm (SAA) are used to determine the optimal position for DPV deployment in the grid with the FCSs. The LVD, computation time, and total power loss for each iteration are compared. The voltage dependence power flow is applied using the backward/forward sweep method (BFS). The LVD is applied to define the objective function of the optimization techniques. The simulation results show that the SAA showed the lowest mean computation time, followed by the GA and the CSA. A possible location of the DPV is bus no. 6 for FCSs with high penetration levels, and the best FCS locations can be found with the GA, with the best percentage of best hit counter on buses no. 2, 3, 13, 14, 28, 15, and 27. Therefore, FCSs can be managed and handled in optimal conditions, and this work supports future FCS expansion.

scholarly journals Adaptive Gene Level Mutation

Algorithms ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 16
Author(s):  
Jalal Al-afandi ◽  
Horváth András
Keyword(s):  
Genetic Algorithms ◽  
Stochastic Optimization ◽  
Specific Problem ◽  
Traditional Approach ◽  
Fitness Function ◽  
Optimization Methods ◽  
Analytic Solutions ◽  
Problem Representation ◽  
Travelling Salesman ◽  
Gene Level

Genetic Algorithms are stochastic optimization methods where solution candidates, complying to a specific problem representation, are evaluated according to a predefined fitness function. These approaches can provide solutions in various tasks even, where analytic solutions can not be or are too complex to be computed. In this paper we will show, how certain set of problems are partially solvable allowing us to grade segments of a solution individually, which results local and individual tuning of mutation parameters for genes. We will demonstrate the efficiency of our method on the N-Queens and travelling salesman problems where we can demonstrate that our approach always results faster convergence and in most cases a lower error than the traditional approach.

Optimization Design of Trusses Based on Covariance Matrix Adaptation Evolution Strategy Algorithm

2012 ◽  
Vol 215-216 ◽  
pp. 133-137
Author(s):  
Guo Shao Su ◽  
Yan Zhang ◽  
Zhen Xing Wu ◽  
Liu Bin Yan
Keyword(s):  
Stochastic Optimization ◽  
Covariance Matrix ◽  
Optimization Problems ◽  
Fitness Function ◽  
Optimization Methods ◽  
Evolution Strategy ◽  
Covariance Matrix Adaptation ◽  
Study Results ◽  
Highly Nonlinear ◽  
Adaptation Evolution

Covariance matrix adaptation evolution strategy algorithm (CMA-ES) is a newly evolution algorithm. It has become a powerful tool for solving highly nonlinear multi-peak optimization problems. In many real-world optimization problems, the location of multiple optima is often required in a search space. In order to evaluate the solution, thousands of fitness function evaluations are involved that is a time consuming or expensive processes. Therefore, conventional stochastic optimization methods meet a special challenge for a very large number of problem function evaluations. Aiming to overcome the shortcoming of stochastic optimization methods in the high calculation cost, a truss optimal method based on CMA-ES algorithm is proposed and applied to solve the section and shape optimization problems of trusses. The study results show that the method is feasible and has the advantages of high accuracy, high efficiency and easy implementation.

Towards Intelligent Road Traffic Management over Weighted Large Graphs Hybrid Meta-heuristic-Based Approach

2022 ◽  
Vol 24 (3) ◽  
pp. 0-0
Keyword(s):  
Traffic Management ◽  
Large Scale ◽  
Road Traffic ◽  
Optimization Technique ◽  
Shortest Paths ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Computation Time ◽  
Exact Algorithm ◽  
On The Road

This paper introduces a new approach of hybrid meta-heuristics based optimization technique for decreasing the computation time of the shortest paths algorithm. The problem of finding the shortest paths is a combinatorial optimization problem which has been well studied from various fields. The number of vehicles on the road has increased incredibly. Therefore, traffic management has become a major problem. We study the traffic network in large scale routing problems as a field of application. The meta-heuristic we propose introduces new hybrid genetic algorithm named IOGA. The problem consists of finding the k optimal paths that minimizes a metric such as distance, time, etc. Testing was performed using an exact algorithm and meta-heuristic algorithm on random generated network instances. Experimental analyses demonstrate the efficiency of our proposed approach in terms of runtime and quality of the result. Empirical results obtained show that the proposed algorithm outperforms some of the existing technique in term of the optimal solution in every generation.

scholarly journals Performance Comparison of Recent Population-Based Metaheuristic Optimisation Algorithms in Mechanical Design Problems of Machinery Components

Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 341
Author(s):  
Bugra Alkan ◽  
Malarvizhi Kaniappan Chinnathai
Keyword(s):  
Engineering Design ◽  
Mechanical Design ◽  
Search Algorithm ◽  
Computation Time ◽  
Population Based ◽  
Performance Comparison ◽  
Design Problems ◽  
Component Design ◽  
Engineering Design Problems ◽  
Optimisation Algorithms

The optimisation of complex engineering design problems is highly challenging due to the consideration of various design variables. To obtain acceptable near-optimal solutions within reasonable computation time, metaheuristics can be employed for such problems. However, a plethora of novel metaheuristic algorithms are developed and constantly improved and hence it is important to evaluate the applicability of the novel optimisation strategies and compare their performance using real-world engineering design problems. Therefore, in this paper, eight recent population-based metaheuristic optimisation algorithms—African Vultures Optimisation Algorithm (AVOA), Crystal Structure Algorithm (CryStAl), Human-Behaviour Based Optimisation (HBBO), Gradient-Based Optimiser (GBO), Gorilla Troops Optimiser (GTO), Runge–Kutta optimiser (RUN), Social Network Search (SNS) and Sparrow Search Algorithm (SSA)—are applied to five different mechanical component design problems and their performance on such problems are compared. The results show that the SNS algorithm is consistent, robust and provides better quality solutions at a relatively fast computation time for the considered design problems. GTO and GBO also show comparable performance across the considered problems and AVOA is the most efficient in terms of computation time.

scholarly journals Online Optimization for Networked Distributed Energy Resources with Time-Coupling Constraints

2019 ◽  
Author(s):  
Shuai Fan ◽  
guangyu he ◽  
Xinyang Zhou ◽  
Mingjian Cui
Keyword(s):  
Optimization Technique ◽  
Optimization Methods ◽  
Distribution Networks ◽  
Distributed Energy ◽  
Lyapunov Optimization ◽  
Operation Conditions ◽  
Coupling Constraints ◽  
Online Implementation ◽  
Algorithmic Framework ◽  
Utility Loss

This paper proposes a Lyapunov optimization-based <a><b> </b></a>online distributed (LOOD) algorithmic framework for active distribution networks with numerous photovoltaic inverters and invert air conditionings (IACs). In the proposed scheme, ADNs can track an active power setpoint reference at the substation in response to transmission-level requests while concurrently minimizing the utility loss and ensuring the security of voltages. In contrast to conventional distributed optimization methods that employ the setpoints for controllable devices only when the algorithm converges, the proposed LOOD can carry out the setpoints immediately relying on the current measurements and operation conditions. Notably, the time-coupling constraints of IACs are decoupled for online implementation with Lyapunov optimization technique. An incentive scheme is tailored to coordinate the customer-owned assets in lieu of the direct control from network operators. Optimality and convergency are characterized analytically. Finally, we corroborate the proposed method on a modified version of 33-node test feeder. <div><br></div>

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