Comparative analysis on optimizing performance of genetic algorithm and programming algorithm for MOTSP

2017 ◽  
Vol 20 (6-7) ◽  
pp. 1533-1536
Author(s):  
Lin Su ◽  
Jingjing Jia
Keyword(s):  
Genetic Algorithm ◽  
Comparative Analysis ◽  
Programming Algorithm

scholarly journals Optimization and comparative analysis of PID and FOPID controller for BLDC motor

2019 ◽  
Vol 8 (3) ◽  
pp. 174
Author(s):  
P. Vimala ◽  
C. R. Balamurugan ◽  
A. Subramanian ◽  
T. Vishwanath
Keyword(s):  
Genetic Algorithm ◽  
Comparative Analysis ◽  
Input Signal ◽  
Pid Controller ◽  
Input Voltage ◽  
Bldc Motor ◽  
Phase Inverter ◽  
Three Phase ◽  
The Stability ◽  
Three Phase Inverter

The FOPID and PID controller are designed to control the speed of <br /> the BLDC motor. The parameters , , , λ and µ of these controller are optimized based on genetic algorithm. The optimized coefficients keep in track with zero error signals. The output of the controller is given to the variable dc source which varies the input voltage to the three phase inverter depending on the input signal. The three phase inverter gives the voltage to the BLDC motor which enhances the stability of the system. <br /> The effectiveness of the controller is demonstrated by simulation.

Tuning a Hybrid Optimization Algorithm by Determining the Modality of the Design Space

2001 ◽  
Author(s):  
Kurt Hacker ◽  
John Eddy ◽  
Kemper Lewis
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Optimization Algorithm ◽  
Design Space ◽  
Hybrid Optimization ◽  
Programming Algorithm ◽  
Sequential Linear Programming ◽  
Hybrid Optimization Algorithm ◽  
Linear Programming Algorithm ◽  
Algorithm Benchmarking

Abstract In this paper we present an approach for increasing the efficiency of a hybrid Genetic/Sequential Linear Programming algorithm. We introduce two metrics for evaluating the modality of the design space and then use this information to efficiently switch between the Genetic Algorithm and SLP algorithm. The motivation for this study is an effort to reduce the computational expense associated with the use of a Genetic Algorithm by reducing the number of function evaluations needed to find good solutions. In the paper the two metrics used to evaluate the modality of the design space are the variance in fitness of the population of the designs in the Genetic Algorithm and the error associated with fitting a response surface to the designs evaluates by the Genetic Algorithm. The effectiveness of this approach is demonstrated by considering a highly multimodal Genetic Algorithm benchmarking problem.

scholarly journals A Genetic Algorithm for Multiobjective Hard Scheduling Optimization

2010 ◽  
Vol 5 (5) ◽  
pp. 825 ◽  
Author(s):  
Elías Niño ◽  
Carlos Ardila ◽  
Alfredo Perez ◽  
Yezid Donoso
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Execution Time ◽  
Level Structure ◽  
Deterministic Algorithm ◽  
Programming Algorithm ◽  
Scheduling Optimization ◽  
Object Oriented Design ◽  
Delivery Times ◽  
Production Orders

<p>This paper proposes a genetic algorithm for multiobjective scheduling optimization based in the object oriented design with constrains on delivery times, process precedence and resource availability. Initially, the programming algorithm (PA) was designed and implemented, taking into account all constraints mentioned. This algorithm’s main objective is, given a sequence of production orders, products and processes, calculate its total programming cost and time.<br /> Once the programming algorithm was defined, the genetic algorithm (GA) was developed for minimizing two objectives: delivery times and total programming cost. The stages defined for this algorithm were: selection, crossover and mutation. During the first stage, the individuals composing the next generation are selected using a strong dominance test. Given the strong restrictions on the model, the crossover stage utilizes a process level structure (PLS) where processes are grouped by its levels in the product tree. Finally during the mutation stage, the solutions are modified in two different ways (selected in a random fashion): changing the selection of the resources of one process and organizing the processes by its execution time by level.<br /> In order to obtain more variability in the found solutions, the production orders and the products are organized with activity planning rules such as EDD, SPT and LPT. For each level of processes, the processes are organized by its processing time from lower to higher (PLU), from higher to lower (PUL), randomly (PR), and by local search (LS). As strategies for local search, three algorithms were implemented: Tabu Search (TS), Simulated Annealing (SA) and Exchange Deterministic Algorithm (EDA). The purpose of the local search is to organize the processes in such a way that minimizes the total execution time of the level.<br /> Finally, Pareto fronts are used to show the obtained results of applying each of the specified strategies. Results are analyzed and compared.</p>

scholarly journals GADP-align: A genetic algorithm and dynamic programming-based method for structural alignment of proteins

Bioimpacts ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 271-279
Author(s):  
Soraya Mirzaei ◽  
Jafar Razmara ◽  
Shahriar Lotfi
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Programming ◽  
Protein Structure ◽  
Hybrid Method ◽  
Structural Alignment ◽  
Dynamic Programming Algorithm ◽  
Structure Alignment ◽  
Programming Algorithm ◽  
Programming Technique ◽  
Iterative Dynamic Programming

Introduction: Similarity analysis of protein structure is considered as a fundamental step to give insight into the relationships between proteins. The primary step in structural alignment is looking for the optimal correspondence between residues of two structures to optimize the scoring function. An exhaustive search for finding such a correspondence between two structures is intractable. Methods: In this paper, a hybrid method is proposed, namely GADP-align, for pairwise protein structure alignment. The proposed method looks for an optimal alignment using a hybrid method based on a genetic algorithm and an iterative dynamic programming technique. To this end, the method first creates an initial map of correspondence between secondary structure elements (SSEs) of two proteins. Then, a genetic algorithm combined with an iterative dynamic programming algorithm is employed to optimize the alignment. Results: The GADP-align algorithm was employed to align 10 ‘difficult to align’ protein pairs in order to evaluate its performance. The experimental study shows that the proposed hybrid method produces highly accurate alignments in comparison with the methods using exactly the dynamic programming technique. Furthermore, the proposed method prevents the local optimal traps caused by the unsuitable initial guess of the corresponding residues. Conclusion: The findings of this paper demonstrate that employing the genetic algorithm along with the dynamic programming technique yields highly accurate alignments between a protein pair by exploring the global alignment and avoiding trapping in local alignments.

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