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 A Novel Algorithm Combining Finite State Method and Genetic Algorithm for Solving Crude Oil Scheduling Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Qian-Qian Duan ◽  
Gen-Ke Yang ◽  
Chang-Chun Pan
Keyword(s):  
Genetic Algorithm ◽  
Crude Oil ◽  
Real Life ◽  
Solution Space ◽  
Partial Solution ◽  
Entire Solution ◽  
Scheduling Problem ◽  
Finite State ◽  
State Method ◽  
Searching Ability

A hybrid optimization algorithm combining finite state method (FSM) and genetic algorithm (GA) is proposed to solve the crude oil scheduling problem. The FSM and GA are combined to take the advantage of each method and compensate deficiencies of individual methods. In the proposed algorithm, the finite state method makes up for the weakness of GA which is poor at local searching ability. The heuristic returned by the FSM can guide the GA algorithm towards good solutions. The idea behind this is that we can generate promising substructure or partial solution by using FSM. Furthermore, the FSM can guarantee that the entire solution space is uniformly covered. Therefore, the combination of the two algorithms has better global performance than the existing GA or FSM which is operated individually. Finally, a real-life crude oil scheduling problem from the literature is used for conducting simulation. The experimental results validate that the proposed method outperforms the state-of-art GA method.

scholarly journals Load Planning of Transport Aircraft Based on Hybrid Genetic Algorithm

2018 ◽  
Vol 179 ◽  
pp. 01007
Author(s):  
Yang Chenguang ◽  
Liu Hu ◽  
Gao Yuan
Keyword(s):  
Genetic Algorithm ◽  
Bin Packing ◽  
Hybrid Genetic Algorithm ◽  
Transport Costs ◽  
Gravity Center ◽  
Transport Aircraft ◽  
Load Planning ◽  
Volume Weight ◽  
Loading Problem

Loading of transport aircraft attracts much attention as the airlift is developing rapidly. It refers to the process that various cargoes are loaded, in an appropriate manner, into kinds of transport aircrafts with constraints of volume, weight and gravity center. Based on two-dimensional bin packing with genetic algorithm (GA), a new hybrid algorithm is proposed to solve the multi-constraint loading problem of transport aircraft for seeking the minimum of fuel consumption. Heuristic algorithm is applied to optimize single-aircraft loading in GA decoding, and the procedure of hybrid GA is summarized for the multi-aircraft loading issues. In the case study, eight kinds of cargos are distributed in three different aircrafts. The optimal result indicates that this algorithm can rapidly generate the best plan for the loading problem regarding lower transport costs.

Optimization Design of Structure and Parameters for RBF Neural Network Using Hybrid Hierarchy Genetic Algorithm

2014 ◽  
Vol 951 ◽  
pp. 274-277 ◽  
Author(s):  
Xu Sheng Gan ◽  
Can Yang ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Optimization Design ◽  
Rbf Neural Network ◽  
Hybrid Genetic Algorithm ◽  
Proposed Model ◽  
Crossover Probability ◽  
Ga Algorithm ◽  
The Hierarchical Structure ◽  
Adaptive Crossover

To improve the optimization design of Radial Basis Function (RBF) neural network, a RBF neural network based on a hybrid Genetic Algorithm (GA) is proposed. First the hierarchical structure and adaptive crossover probability is introduced into the traditional GA algorithm for the improvement, and then the hybrid GA algorithm is used to optimize the structure and parameters of the network. The simulation indicates that the proposed model has a good modeling performance.

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.

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.

Iterative genetic algorithm for learning efficient fuzzy rule set

2003 ◽  
Vol 17 (4) ◽  
pp. 335-347 ◽  
Author(s):  
MENG HIOT LIM ◽  
WILLIE NG
Keyword(s):  
Genetic Algorithm ◽  
Industrial Process ◽  
Solution Space ◽  
Fuzzy Rule ◽  
Bath Temperature ◽  
Entire Solution ◽  
Linguistic Terms ◽  
Learning Module ◽  
Rule Set ◽  
Fuzzy Control Algorithm

We present a methodology of learning fuzzy rules using an iterative genetic algorithm (GA). The approach incorporates a scheme of partitioning the entire solution space into individual subspaces. It then employs a mechanism to progressively relax or tighten the constraint. The relaxation or tightening of constraint guides the GA to the subspace for further iteration. The system referred to as the iterative GA learning module is useful for learning an efficient fuzzy control algorithm based on a predefined linguistic terms set. The overall approach was applied to learn a fuzzy algorithm for a water bath temperature control. The simulation results demonstrate the effectiveness of the approach in automating an industrial process.

scholarly journals OPTIMIZATION OF ASSOCIATION RULES FOR TUBERCULOSIS USING GENETIC ALGORITHM

2014 ◽  
pp. 151-159
Author(s):  
T. Asha ◽  
S. Natarajan ◽  
K.N.B. Murthy
Keyword(s):  
Genetic Algorithm ◽  
Association Rule ◽  
Association Rule Mining ◽  
Solution Space ◽  
Current Approach ◽  
Experimental Result ◽  
Rule Mining ◽  
Human Immuno Deficiency Virus ◽  
Small Set ◽  
High Level

Tuberculosis (TB) is a disease caused by bacteria called Mycobacterium Tuberculosis which usually spreads through the air and attacks low immune bodies. Human Immuno deficiency Virus (HIV) patients are more likely to be attacked by TB. It is an important health problem around the world including India. Association Rule Mining is the process of discovering interesting and unexpected rules from large sets of data. This approach results in huge quantity of rules where some are interesting and others are repetitive. It also limits the quality of rules to only two measures support and confidence. In this paper we try to optimize the rules generated by Association Rule Mining for Tuberculosis using Genetic Algorithm. Our approach is to extract only a small set of high quality Tuberculosis rules among the larger set using Genetic Algorithm. In the current approach datatypes such as discrete, continuous and categorical items have been handled. The proposed experimental result includes a small set of converged TB rules that helps doctors in their diagnosis decisions. The main motivation for using Genetic Algorithms in the discovery of high-level prediction rules is that they are robust, use adaptive search techniques that perform a global search on the solution space and cope better with attribute interaction than the greedy rule induction algorithms often used in data mining.

Genetic Algorithm Based on Chaos Optimization

2010 ◽  
Vol 439-440 ◽  
pp. 641-645
Author(s):  
Chun Bo Xiu ◽  
Li Fen Lu ◽  
Yi Cheng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Global Solutions ◽  
Function Optimization ◽  
Chaos Optimization ◽  
Fast Convergence Rate ◽  
Simulation Results ◽  
Searching Ability ◽  
Two Stages

A hybrid genetic algorithm is proposed based on chaos optimization. The optimization process can be divided into two stages every iteration, one is genetic coarse searching and the other is chaos elaborate searching. Genetic algorithm searches the global solutions in the origin space. An elaborate space near the center of superior individuals is divided from the origin space, which is searched by chaos optimization adequately to generate new better superior individuals for genetic operation. The elaborate space can be compressed quickly to accelerate searching rate and enhance the searching efficiency. In this way, the algorithm has global searching ability and fast convergence rate. The simulation results prove that the algorithm can give satisfied results to function optimization problems.

scholarly journals GA-BP in Thermal Fatigue Failure Prediction of Microelectronic Chips

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 542 ◽  
Author(s):  
Zhongying Han ◽  
Xiaoguang Huang
Keyword(s):  
Genetic Algorithm ◽  
Prediction Model ◽  
Thermal Fatigue ◽  
Life Prediction ◽  
Hybrid Genetic Algorithm ◽  
Back Propagation ◽  
Fem Analysis ◽  
Bp Network ◽  
Searching Ability ◽  
Life Prediction Model

A thermal fatigue life prediction model of microelectronic chips based on thermal fatigue tests and solder/substrate interfacial singularity analysis from finite element method (FEM) analysis is established in this paper. To save the calculation of interfacial singular parameters of new chips for life prediction, and improve the accuracy of prediction results in actual applications, a hybrid genetic algorithm–artificial neural network (GA–ANN) strategy is utilized. The proposed algorithm combines the local searching ability of the gradient-based back propagation (BP) strategy with the global searching ability of a genetic algorithm. A series of combinations of the dimensions and thermal mechanical properties of the solder and the corresponding singularity parameters at the failure interface are used to train the proposed GA-BP network. The results of the network, together with the established life prediction model, are used to predict the thermal fatigue lives of new chips. The comparison between the network results and thermal fatigue lives recorded in experiments shows that the GA-BP strategy is a successful prediction technique.

Sign in / Sign up

Export Citation Format

Share Document