A Tabu-Gradient Search-Based Optimization Technique for Synthesis of Mechanisms

2004 ◽  
Author(s):  
Ahmad Smaili ◽  
Naji Atallah
Keyword(s):  
Tabu Search ◽  
Global Minimum ◽  
Short Term Memory ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Technique ◽  
Optimization Methods ◽  
Gradient Search ◽  
Optimum Synthesis ◽  
Timing Task

Mechanism synthesis requires the use of optimization methods to obtain approximate solution whenever the desired number of positions the mechanism is required to traverse exceeds a few (five in a 4R linkage). Deterministic gradient-based methods are usually impractical when used alone because they move in the direction of local minima. Random search methods on the other hand have a better chance of converging to a global minimum. This paper presents a tabu-gradient search based method for optimum synthesis of planar mechanisms. Using recency-based short-term memory strategy, tabu-search is initially used to find a solution near global minimum, followed by a gradient search to move the solution ever closer to the global minimum. A brief review of tabu search method is presented. Then, tabu-gradient search algorithm is applied to synthesize a four-bar mechanism for a 10-point path generation with prescribed timing task. As expected, Tabu-gradient base search resulted in a better solution with less number of iterations and shorter run-time.

Discrete-Continuous Configuration Optimization Methods for Structures Using the Harmony Search Algorithm

2006 ◽  
Vol 324-325 ◽  
pp. 1293-1296 ◽  
Author(s):  
K.S. Lee ◽  
Chang Sik Choi
Keyword(s):  
Search Algorithm ◽  
Random Search ◽  
Optimization Technique ◽  
Harmony Search ◽  
Optimization Methods ◽  
Harmony Search Algorithm ◽  
Perfect State ◽  
Gradient Search ◽  
Geometric Variables ◽  
Derivative Information

This paper proposes an efficient structural optimization methods based on the harmony search (HS) heuristic algorithm that treat integrated discrete sizing and continuous geometric variables. The recently developed HS algorithm was conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so derivative information is unnecessary. A benchmark truss example is presented to demonstrate the effectiveness and robustness of the new method, as compared to current optimization methods. The numerical results reveal that the proposed method is a powerful search and design optimization technique for structures with discrete member sizes, and may yield better solutions than those obtained using current methods.

Optimum Synthesis of Mechanisms Using Tabu-Gradient Search Algorithm

2004 ◽  
Vol 127 (5) ◽  
pp. 917-923 ◽  
Author(s):  
Ahmad A. Smaili ◽  
Nadim A. Diab ◽  
Naji A. Atallah
Keyword(s):  
Tabu Search ◽  
Short Term Memory ◽  
Search Algorithm ◽  
Gradient Algorithm ◽  
Gradient Search ◽  
Planar Mechanisms ◽  
Optimum Synthesis ◽  
Synthesis Of Mechanisms ◽  
Tabu Search Method ◽  
Optimization Schemes

A tabu-gradient search is herein presented for optimum synthesis of planar mechanisms. The solution generated by a recency-based, short term memory tabu search is used to start a gradient search to drive the solution ever closer to the global minimum. A brief overview of the tabu-search method is first presented. A tabu-gradient algorithm is then used to synthesize four-bar mechanisms for path generation tasks by way of three examples, including two benchmark examples used before to test other deterministic and intelligent optimization schemes. Compared with the corresponding results generated by other schemes, the tabu-gradient search rendered the most optimal solutions of all.

Optimum Synthesis of Rigid Mechanisms Using Real Coded Quantum-Inspired Evolution Algorithm (RQIEA) With Neighborhood Search

2007 ◽  
Author(s):  
Ahmad Smaili ◽  
Mazen Hassanieh ◽  
Bachir Chaaya ◽  
Fawzan Al Fares
Keyword(s):  
Quantum Computing ◽  
Search Algorithm ◽  
Random Search ◽  
Quantum Gates ◽  
Neighborhood Search ◽  
Gradient Search ◽  
Quantum Bits ◽  
Optimum Synthesis ◽  
Evolution Algorithm ◽  
Optimization Schemes

A modified real coded quantum-inspired evolution algorithm (MRQIEA) is herein presented for optimum synthesis of planar rigid body mechanisms (RBMs). The MRQIEA employs elements of quantum computing such as quantum bits, registers, and quantum gates, neighborhood search engine, and gradient search to form a random search algorithm for solution optimization of a wide class of problems. A brief overview of the quantum computing elements and their adaptation to the optimization algorithm is first presented. The algorithm is then adapted to the synthesis problem of RBMs. Finally, the algorithm is demonstrated and compared to other search methods by way of three examples, including two benchmark examples that have been used in the literature to assess the performance of other optimization schemes.

scholarly journals Design of Flotation Circuits Using Tabu-Search Algorithms: Multispecies, Equipment Design, and Profitability Parameters

Minerals ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 181 ◽  
Author(s):  
Freddy Lucay ◽  
Edelmira Gálvez ◽  
Luis Cisternas
Keyword(s):  
Tabu Search ◽  
Approximate Method ◽  
Short Term Memory ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Adaptive Procedure ◽  
Problem Size ◽  
Combinatorial Optimization Problems

The design of a flotation circuit based on optimization techniques requires a superstructure for representing a set of alternatives, a mathematical model for modeling the alternatives, and an optimization technique for solving the problem. The optimization techniques are classified into exact and approximate methods. The first has been widely used. However, the probability of finding an optimal solution decreases when the problem size increases. Genetic algorithms have been the approximate method used for designing flotation circuits when the studied problems were small. The Tabu-search algorithm (TSA) is an approximate method used for solving combinatorial optimization problems. This algorithm is an adaptive procedure that has the ability to employ many other methods. The TSA uses short-term memory to prevent the algorithm from being trapped in cycles. The TSA has many practical advantages but has not been used for designing flotation circuits. We propose using the TSA for solving the flotation circuit design problem. The TSA implemented in this work applies diversification and intensification strategies: diversification is used for exploring new regions, and intensification for exploring regions close to a good solution. Four cases were analyzed to demonstrate the applicability of the algorithm: different objective function, different mathematical models, and a benchmarking between TSA and Baron solver. The results indicate that the developed algorithm presents the ability to converge to a solution optimal or near optimal for a complex combination of requirements and constraints, whereas other methods do not. TSA and the Baron solver provide similar designs, but TSA is faster. We conclude that the developed TSA could be useful in the design of full-scale concentration circuits.

A Quick Presentation of Evolutionary Computation

2010 ◽  
pp. 22-38
Author(s):  
Pierre Collet
Keyword(s):  
Evolutionary Computation ◽  
Random Search ◽  
Optimization Technique ◽  
Parallel Architecture ◽  
Optimization Methods ◽  
General Purpose ◽  
Old Field ◽  
Multiple Data ◽  
Intractable Problems ◽  
The 1960S

Evolutionary computation is an old field of computer science, that started in the 1960s nearly simultaneously in different parts of the world. It is an optimization technique that mimics the principles of Darwinian evolution in order to find good solutions to intractable problems faster than a random search. Artificial Evolution is only one among many stochastic optimization methods, but recently developed hardware (General Purpose Graphic Processing Units or GPGPU) gives it a tremendous edge over all the other algorithms, because its inherently parallel nature can directly benefit from the difficult to use Single Instruction Multiple Data parallel architecture of these cheap, yet very powerful cards.

scholarly journals A bi-level optimization model for grouping constrained storage location assignment problems

2021 ◽  
Author(s):  
Jing Xie ◽  
Yi Mei ◽  
Andreas T Ernst ◽  
Xiaodong Li ◽  
Andy Song
Keyword(s):  
Tabu Search ◽  
Search Algorithm ◽  
Random Search ◽  
Search Space ◽  
Search Method ◽  
Fitness Evaluation ◽  
Random Search Method ◽  
Storage Location ◽  
Location Assignment ◽  
Upper Level

In this paper, a novel bi-level grouping optimization (BIGO) model is proposed for solving the storage location assignment problem with grouping constraint (SLAP-GC). A major challenge in this problem is the grouping constraint which restricts the number of groups each product can have and the locations of items in the same group. In SLAP-GC, the problem consists of two subproblems, one is how to group the items, and the other one is how to assign the groups to locations. It is an arduous task to solve the two subproblems simultaneously. To overcome this difficulty, we propose a BIGO. BIGO optimizes item grouping in the upper level, and uses the lower-level optimization to evaluate each item grouping. Sophisticated fitness evaluation and search operators are designed for both upper and lower level optimization so that the feasibility of solutions can be guaranteed, and the search can focus on promising areas in the search space. Based on the BIGO model, a multistart random search method and a tabu search algorithm are proposed. The experimental results on the real-world dataset validate the efficacy of the BIGO model and the advantage of the tabu search method over the random search method. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

scholarly journals Genetic Algorithm – A Sensible Evolutionary Optimization Technique

2019 ◽  
Vol 8 (9) ◽  
pp. 2805-2809
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Technique ◽  
Search Space ◽  
Effective Population ◽  
Natural Systems ◽  
Heuristic Search Algorithm ◽  
Feasible Solutions

The study presents a pragmatic outlook of genetic algorithm. Many biological algorithms are inspired for their ability to evolve towards best solutions and of all; genetic algorithm is widely accepted as they well suit evolutionary computing models. Genetic algorithm could generate optimal solutions on random as well as deterministic problems. Genetic algorithm is a mathematical approach to imitate the processes studied in natural evolution. The methodology of genetic algorithm is intensively experimented in order to use the power of evolution to solve optimization problems. Genetic algorithm is an adaptive heuristic search algorithm based on the evolutionary ideas of genetics and natural selection. Genetic algorithm exploits random search approach to solve optimization problems. Genetic algorithm takes benefits of historical information to direct the search into the convergence of better performance within the search space. The basic techniques of evolutionary algorithms are observed to be simulating the processes in natural systems. These techniques are aimed to carry effective population to the next generation and ensure the survival of the fittest. Nature supports the domination of stronger over the weaker ones in any kind. In this study, we proposed the arithmetic views of the behavior and operators of genetic algorithm that support the evolution of feasible solutions to optimized solutions.

Path Relinking with Multi-Start Tabu Search for the Quadratic Assignment Problem

2013 ◽  
pp. 52-70
Author(s):  
Tabitha James ◽  
Cesar Rego
Keyword(s):  
Particle Swarm Optimization ◽  
Tabu Search ◽  
Assignment Problem ◽  
Search Algorithm ◽  
Quadratic Assignment Problem ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Path Relinking ◽  
Quadratic Assignment ◽  
Swarm Optimization

This paper introduces a new path relinking algorithm for the well-known quadratic assignment problem (QAP) in combinatorial optimization. The QAP has attracted considerable attention in research because of its complexity and its applicability to many domains. The algorithm presented in this study employs path relinking as a solution combination method incorporating a multistart tabu search algorithm as an improvement method. The resulting algorithm has interesting similarities and contrasts with particle swarm optimization methods. Computational testing indicates that this algorithm produces results that rival the best QAP algorithms. The authors additionally conduct an analysis disclosing how different strategies prove more or less effective depending on the landscapes of the problems to which they are applied. This analysis lays a foundation for developing more effective future QAP algorithms, both for methods based on path relinking and tabu search, and for hybrids of such methods with related processes found in particle swarm optimization.

scholarly journals A bi-level optimization model for grouping constrained storage location assignment problems

2021 ◽  
Author(s):  
Jing Xie ◽  
Yi Mei ◽  
Andreas T Ernst ◽  
Xiaodong Li ◽  
Andy Song
Keyword(s):  
Tabu Search ◽  
Search Algorithm ◽  
Random Search ◽  
Search Space ◽  
Search Method ◽  
Fitness Evaluation ◽  
Random Search Method ◽  
Storage Location ◽  
Location Assignment ◽  
Upper Level

In this paper, a novel bi-level grouping optimization (BIGO) model is proposed for solving the storage location assignment problem with grouping constraint (SLAP-GC). A major challenge in this problem is the grouping constraint which restricts the number of groups each product can have and the locations of items in the same group. In SLAP-GC, the problem consists of two subproblems, one is how to group the items, and the other one is how to assign the groups to locations. It is an arduous task to solve the two subproblems simultaneously. To overcome this difficulty, we propose a BIGO. BIGO optimizes item grouping in the upper level, and uses the lower-level optimization to evaluate each item grouping. Sophisticated fitness evaluation and search operators are designed for both upper and lower level optimization so that the feasibility of solutions can be guaranteed, and the search can focus on promising areas in the search space. Based on the BIGO model, a multistart random search method and a tabu search algorithm are proposed. The experimental results on the real-world dataset validate the efficacy of the BIGO model and the advantage of the tabu search method over the random search method. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

A Novel Machine-Learning Aided Optimization Technique for Material Design: Application in Thin Film Solar Cells

2016 ◽  
Author(s):  
Shima Hajimirza
Keyword(s):  
Thin Film ◽  
Machine Learning ◽  
Objective Function ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Technique ◽  
Global Search ◽  
Radiative Properties ◽  
Mcmc Methods ◽  
Predictive Tool

Patterned thin film structures can offer spectrally selective radiative properties that benefit many engineering applications including photovoltaic energy conversion at extremely efficient scales. Inverse design of such structures can be expressed as an interesting optimization problem with a specific regime of complexity; namely moderate number of optimization parameters but highly time-consuming forward problem. For problems like this, a search technique that can somehow learn and parameterize the multi-dimensional behavior of the objective function based on past search points can be extremely useful in guiding the global search algorithm and expediting the solution for such complexity regimes. Based on this idea, we have developed a novel search algorithm for optimizing absorption coefficient of visible light in a multi-layered silicon-based nano-scale thin film solar cell. The proposed optimization algorithm uses a machine-learning predictive tool called regression-tree in an intermediary step to learn (i.e. regress) the objective function based on a previous generation of random search points. The fitted model is then used as a guide to resample from a new generation of candidate solutions with a significantly higher average gain. This process can be repeated multiple times and better solutions are obtained with high likelihood at each stage. Through numerical experiments we demonstrate how in only one resampling stage, the propose technique dominates the state-of-the-art global search algorithms such as gradient based techniques or MCMC methods in the considered nano-design problem.

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