Finding Diversity in Mechanisms Using a Hybrid Genetic Algorithm

2000 ◽  
Author(s):  
Robert A. O’Neil ◽  
Louis J. Everett
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Solution Space ◽  
Global Optimum ◽  
Small Error ◽  
Test Function ◽  
Modified Genetic Algorithm ◽  
Path Synthesis ◽  
Alternative Solutions ◽  
Better Than

Abstract The path synthesis problem for mechanical linkages still presents problems for engineers, although it has been examined for more than two centuries. This research approached the design problem as one of creating a characteristic test function to compare a synthesized output path with a desired output path, and finding a set of linkages that reduce the corresponding error. Since the solution space of this approach is very large with typically a generous number of local minima, it may be possible to find several linkages that each produce a small error. This research investigated the ability to use a modified genetic algorithm to search for a global minima and simultaneously identify several linkage designs that are “almost” as good as the global optimum. Having alternative solutions will allow designers to choose a mechanism that best fits criteria other than path error. The results from using the method on a subclass of linkage problems demonstrate that solutions can be found that “fit” better than those found in the literature. The results also show that a diverse family of acceptable designs can be obtained and that this family includes both “well known” designs and heretofore unknown solutions.

Artificial Life Optimization Algorithm and Applications

2011 ◽  
pp. 423-449
Author(s):  
Bo-Suk Yang
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Artificial Life ◽  
Optimization Problem ◽  
Global Optimum ◽  
Function Optimization ◽  
Global Function ◽  
Test Function ◽  
Optimum Solution ◽  
Artificial World

This chapter describes a hybrid artificial life optimization algorithm (ALRT) based on emergent colonization to compute the solutions of global function optimization problem. In the ALRT, the emergent colony is a fundamental mechanism to search the optimum solution and can be accomplished through the metabolism, movement and reproduction among artificial organisms which appear at the optimum locations in the artificial world. In this case, the optimum locations mean the optimum solutions in the optimization problem. Hence, the ALRT focuses on the searching for the optimum solution in the location of emergent colonies and can achieve more accurate global optimum. The optimization results using different types of test functions are presented to demonstrate the described approach successfully achieves optimum performance. The algorithm is also applied to the test function optimization and optimum design of short journal bearing as a practical application. The optimized results are compared with those of genetic algorithm and successive quadratic programming to identify the optimizing ability.

Improved Method of Hybrid Genetic Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 4014-4017
Author(s):  
Lei Ding ◽  
Yong Jun Luo ◽  
Yang Yang Wang ◽  
Zheng Li ◽  
Bing Yin Yao
Keyword(s):  
Genetic Algorithm ◽  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Local Optimum ◽  
Global Optimal Solution ◽  
Global Optimal ◽  
Better Than

On account of low convergence of the traditional genetic algorithm in the late,a hybrid genetic algorithm based on conjugate gradient method and genetic algorithm is proposed.This hybrid algorithm takes advantage of Conjugate Gradient’s certainty, but also the use of genetic algorithms in order to avoid falling into local optimum, so it can quickly converge to the exact global optimal solution. Using Two test functions for testing, shows that performance of this hybrid genetic algorithm is better than single conjugate gradient method and genetic algorithm and have achieved good results.

scholarly journals Optimal source localization problem based on TOA measurements

2017 ◽  
Vol 14 (1) ◽  
pp. 161-176
Author(s):  
Maja Rosic ◽  
Mirjana Simic ◽  
Predrag Pejovic ◽  
Milan Bjelica
Keyword(s):  
Genetic Algorithm ◽  
Lower Bound ◽  
Signal To Noise Ratio ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Nonlinear Least Squares ◽  
Global Optimum ◽  
Time Of Arrival ◽  
Newton Raphson ◽  
Optimum Solution

Determining an optimal emitting source location based on the time of arrival (TOA) measurements is one of the important problems in Wireless Sensor Networks (WSNs). The nonlinear least-squares (NLS) estimation technique is employed to obtain the location of an emitting source. This optimization problem has been formulated by the minimization of the sum of squared residuals between estimated and measured data as the objective function. This paper presents a hybridization of Genetic Algorithm (GA) for the determination of the global optimum solution with the local search Newton-Raphson (NR) method. The corresponding Cramer-Rao lower bound (CRLB) on the localization errors is derived, which gives a lower bound on the variance of any unbiased estimator. Simulation results under different signal-to-noise-ratio (SNR) conditions show that the proposed hybrid Genetic Algorithm-Newton-Raphson (GA-NR) improves the accuracy and efficiency of the optimal solution compared to the regular GA.

RDF Query Path Optimization Using Hybrid Genetic Algorithms

2022 ◽  
Vol 12 (1) ◽  
pp. 1-16
Author(s):  
Qazi Mudassar Ilyas ◽  
Muneer Ahmad ◽  
Sonia Rauf ◽  
Danish Irfan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Execution Time ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Solution Space ◽  
Path Optimization ◽  
Initial Population ◽  
Description Framework ◽  
The Cost

Resource Description Framework (RDF) inherently supports data mergers from various resources into a single federated graph that can become very large even for an application of modest size. This results in severe performance degradation in the execution of RDF queries. As every RDF query essentially traverses a graph to find the output of the Query, an efficient path traversal reduces the execution time of RDF queries. Hence, query path optimization is required to reduce the execution time as well as the cost of a query. Query path optimization is an NP-hard problem that cannot be solved in polynomial time. Genetic algorithms have proven to be very useful in optimization problems. We propose a hybrid genetic algorithm for query path optimization. The proposed algorithm selects an initial population using iterative improvement thus reducing the initial solution space for the genetic algorithm. The proposed algorithm makes significant improvements in the overall performance. We show that the overall number of joins for complex queries is reduced considerably, resulting in reduced cost.

scholarly journals The Effect of Entropy on the Performance of Modified Genetic Algorithm Using Earthquake and Wind Time Series

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Manuel Vargas ◽  
Guillermo Fuertes ◽  
Miguel Alfaro ◽  
Gustavo Gatica ◽  
Sebastian Gutierrez ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Genetic Algorithms ◽  
Global Optimum ◽  
Natural Phenomena ◽  
Mathematical Functions ◽  
Dynamic Complexity ◽  
Modified Genetic Algorithm

The dynamic complexity of time series of natural phenomena allowed to improve the performance of the genetic algorithm to optimize the test mathematical functions. The initial populations of stochastic origin of the genetic algorithm were replaced using the series of time of winds and earthquakes. The determinism of the time series brings in more information in the search of the global optimum of the functions, achieving reductions of time and an improvement of the results. The information of the initial populations was measured using the entropy of Shannon and allowed to establish the importance of the entropy in the initial populations and its relation with getting better results. This research establishes a new methodology for using determinism time series to search the best performance of the models of optimization of genetic algorithms (GA).

A Hybrid Genetic Algorithm for Shipment Loading Problem with Consideration of Gas Emission

2014 ◽  
Vol 687-691 ◽  
pp. 5069-5074
Author(s):  
Can Tao Shi ◽  
Lu Xin Liu ◽  
Zhi Wei Luan ◽  
Zhen Wang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Solution Space ◽  
Entire Solution ◽  
Experimental Result ◽  
Gas Emission ◽  
Proposed Model ◽  
Loading Problem ◽  
Searching Ability ◽  
Operation Cost

For shipment loading problem, a mathematical model is established with objective of minimizing operation cost mainly led from gas emission. The genetic algorithm is applied to solve it with modifications: a segmented chromosome coding is adopted to represent the entire solution space; crossover operator and mutation operator are re-defined to make genetic algorithm suitable for the problem; a repair algorithm for infeasible solution is designed to improve the searching ability and increase the converging speed. The experimental result indicates that the proposed model and algorithm are feasible and effective.

scholarly journals The Multiagent Planning Problem

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Tamás Kalmár-Nagy ◽  
Giovanni Giardini ◽  
Bendegúz Dezső Bak
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Problem Solver ◽  
Planning Problem ◽  
Genetic Operators ◽  
Total Distance ◽  
Modified Genetic Algorithm ◽  
Multiagent Planning ◽  
Simple Paths ◽  
Better Than

The classical Multiple Traveling Salesmen Problem is a well-studied optimization problem. Given a set ofngoals/targets andmagents, the objective is to findmround trips, such that each target is visited only once and by only one agent, and the total distance of these round trips is minimal. In this paper we describe the Multiagent Planning Problem, a variant of the classical Multiple Traveling Salesmen Problem: given a set ofngoals/targets and a team ofmagents,msubtours (simple paths) are sought such that each target is visited only once and by only one agent. We optimize for minimum time rather than minimum total distance; therefore the objective is to find the Team Plan in which the longest subtour is as short as possible (a min–max problem). We propose an easy to implement Genetic Algorithm Inspired Descent (GAID) method which evolves a set of subtours using genetic operators. We benchmarked GAID against other evolutionary algorithms and heuristics. GAID outperformed the Ant Colony Optimization and the Modified Genetic Algorithm. Even though the heuristics specifically developed for Multiple Traveling Salesmen Problem (e.g.,k-split, bisection) outperformed GAID, these methods cannot solve the Multiagent Planning Problem. GAID proved to be much better than an open-source Matlab Multiple Traveling Salesmen Problem solver.

scholarly journals Limited Search Space-Based Algorithm for Dual Resource Constrained Scheduling Problem with Multilevel Product Structure

2019 ◽  
Vol 9 (19) ◽  
pp. 4005 ◽  
Author(s):  
Geunho Yang ◽  
Byung Do Chung ◽  
Sang Jin Lee
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Solution Space ◽  
Search Space ◽  
Product Structure ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Resource Constrained

This study addresses the dual resource constrained flexible job shop scheduling problem (DRCFJSP) with a multilevel product structure. The DRCFJSP is a strong NP-hard problem, and an efficient algorithm is essential for DRCFJSP. In this study, we propose an algorithm for the DRCFJSP with a multilevel product structure to minimize the lateness, makespan, and deviation of the workload with preemptive priorities. To efficiently solve the problem within a limited time, the search space is limited based on the possible start and end time, and focus is placed on the intensification rather than diversification, which can help the algorithm spend more time to find an optimal solution in a reasonable solution space. The performance of the proposed algorithm is compared with those of a genetic algorithm and a hybrid genetic algorithm with variable neighborhood search. The numerical experiments demonstrate that the strategy limiting the search space is effective for large and complex problems.

scholarly journals Computing Near-Optimal Solutions to the Steiner Problem in a Graph Using a Genetic Algorithm

1994 ◽  
Vol 23 (468) ◽  
Author(s):  
Henrik Esbensen
Keyword(s):  
Genetic Algorithm ◽  
Steiner Tree ◽  
Global Optimum ◽  
Branch And Cut ◽  
Average Error ◽  
Steiner Problem ◽  
Error Ratio ◽  
Order Of Magnitude ◽  
The Cost ◽  
Better Than

<p>A new Genetic Algorithm (GA) for the Steiner Problem in a Graph (SPG) is presented. The algorithm is based on a bitstring encoding. A bitstring specifies selected Steiner vertices and the corresponding Steiner tree is computed using the Distance Network Heuristic. This scheme ensures that every bitstring correspond to a valid Steiner tree and thus eliminates the need for penalty terms in the cost function.</p><p> </p><p>The GA is tested on all SPG instances from the OR-Library of which the largest graphs have 2,500 vertices and 62,500 edges. When executed 10 times on each of 58 graph examples, the GA finds the global optimum at least once for 55 graphs and every time for 43 graphs. In total the GA finds the global optimum in 77 % of all program executions and is within 1 % from the global optimum in more than 92 % of all executions.</p><p> </p><p>The performance is compared to that of two branch-and-cut algorithms and one of the very best deterministic heuristics, an iterated version of the Shortest Path Heuristic (SPH-I). For all test examples but one, even the worst result ever found by the GA is equal to or better than the result of SPH-I and in many cases the average error ratio of the GA is an order of magnitude better than that of SPH-I. The runtime of the GA is moderate for all test examples. This is in contrast to SPH-I as well as the branch-and-cut algorithms, for which the runtime in some cases are extremely high.</p>

A Hybrid Biomimetic Genetic Algorithm Using a Local Fuzzy Simplex Search

2010 ◽  
Author(s):  
George S. Ladkany ◽  
Mohamed B. Trabia
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Rate Of Convergence ◽  
Genetic Information ◽  
Hybrid Genetic Algorithm ◽  
Global Optimum ◽  
Benchmark Problems ◽  
Simplex Search ◽  
Successful Approach ◽  
Comparison Of The Results

This paper presents a hybrid genetic algorithm that expands upon the previously successful approach of twinkling genetic algorithm (TGA) by incorporating a highly efficient local fuzzy-simplex search within the algorithm. The TGA was in principle a bio-mimetic algorithm that introduced a controlled deviation from a typical GA method, by not requiring that every genevariable of an offspring be the result of a crossover. Instead, twinkling allowed the genetic information of the randomly chosen gene locations to be directly passed on from one parent, which was shown to increase the likelihood of survival of a successful gene value within the offspring, rather than requiring it to be blended. The twinkling genetic algorithms proved highly effective at locating exact global optimum with a competitive rate of convergence for a wide variety of benchmark problems. In this work, it is proposed to couple the TGA with a fuzzy simplex local search to increase the rate of convergence of the algorithm. The proposed algorithm is tested using common mathematical and engineering design benchmark problems. Comparison of the results of this algorithm with earlier algorithms is presented.

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