Genetic Algorithm for Parameters Tuning of Two Stage Switching Controller for UAV Autonomous Formation Flight

2021 ◽  
pp. 154-165
Author(s):  
Arkadiusz Bożko ◽  
Leszek Ambroziak ◽  
Ewa Pawluszewicz
Keyword(s):  
Genetic Algorithm ◽  
Formation Flight ◽  
Two Stage ◽  
Parameters Tuning ◽  
Switching Controller

scholarly journals A Two-Stage Mono- and Multi-Objective Method for the Optimization of General UPS Parallel Manipulators

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 543
Author(s):  
Alejandra Ríos ◽  
Eusebio E. Hernández ◽  
S. Ivvan Valdez
Keyword(s):  
Genetic Algorithm ◽  
Performance Metrics ◽  
Tracking Error ◽  
Parallel Manipulators ◽  
Optimization Method ◽  
Statistical Hypothesis ◽  
Two Stage ◽  
Multi Objective ◽  
Second Stage ◽  
Conflicting Objectives

This paper introduces a two-stage method based on bio-inspired algorithms for the design optimization of a class of general Stewart platforms. The first stage performs a mono-objective optimization in order to reach, with sufficient dexterity, a regular target workspace while minimizing the elements’ lengths. For this optimization problem, we compare three bio-inspired algorithms: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), and the Boltzman Univariate Marginal Distribution Algorithm (BUMDA). The second stage looks for the most suitable gains of a Proportional Integral Derivative (PID) control via the minimization of two conflicting objectives: one based on energy consumption and the tracking error of a target trajectory. To this effect, we compare two multi-objective algorithms: the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The main contributions lie in the optimization model, the proposal of a two-stage optimization method, and the findings of the performance of different bio-inspired algorithms for each stage. Furthermore, we show optimized designs delivered by the proposed method and provide directions for the best-performing algorithms through performance metrics and statistical hypothesis tests.

A Two-Stage Hybrid Algorithm for Flexible Job-Shop Scheduling

2011 ◽  
Vol 268-270 ◽  
pp. 476-481
Author(s):  
Li Gao ◽  
Ke Lin Xu ◽  
Wei Zhu ◽  
Na Na Yang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Job Shop ◽  
Flow Shop ◽  
Job Shop Scheduling ◽  
Selection Procedure ◽  
Dynamic Programming Method ◽  
Two Stage ◽  
Simulated Annealing Genetic Algorithm ◽  
Mutation Operators

A mathematical model was constructed with two objectives. A two-stage hybrid algorithm was developed for solving this problem. At first, the man-hour optimization based on genetic algorithm and dynamic programming method, the model decomposes the flow shop into two layers: sub-layer and patrilineal layer. On the basis of the man-hour optimization，A simulated annealing genetic algorithm was proposed to optimize the sequence of operations. A new selection procedure was proposed and hybrid crossover operators and mutation operators were adopted. A benchmark problem solving result indicates that the proposed algorithm is effective.

Memetic Algorithm Approach to Two-Stage Hybrid Flow Shop Scheduling Problem with Identical Parallel Machines

2013 ◽  
Vol 651 ◽  
pp. 548-552
Author(s):  
Parinya Kaweegitbundit
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Parallel Machines ◽  
Flow Shop ◽  
Memetic Algorithm ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problems ◽  
Two Stage ◽  
Hybrid Flow Shop Scheduling

This paper considers two stage hybrid flow shop (HFS) with identical parallel machine. The objectives is to determine makespan have been minimized. This paper presented memetic algorithm procedure to solve two stage HFS problems. To evaluated performance of propose method, the results have been compared with two meta-heuristic, genetic algorithm, simulated annealing. The experimental results show that propose method is more effective and efficient than genetic algorithm and simulated annealing to solve two stage HFS scheduling problems.

A simple and effective genetic algorithm for the two-stage capacitated facility location problem

2014 ◽  
Vol 75 ◽  
pp. 200-208 ◽  
Author(s):  
Diogo R.M. Fernandes ◽  
Caroline Rocha ◽  
Daniel Aloise ◽  
Glaydston M. Ribeiro ◽  
Enilson M. Santos ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Facility Location ◽  
Location Problem ◽  
Facility Location Problem ◽  
Two Stage ◽  
Capacitated Facility Location Problem ◽  
Capacitated Facility Location

scholarly journals A hybrid based genetic algorithm for solving a capacitated fixed-charge transportation problem

2016 ◽  
Vol 32 (2) ◽  
pp. 225-232
Author(s):  
PETRICA POP ◽  
◽  
OLIVIU MATEI ◽  
CORINA POP SITAR ◽  
IOANA ZELINA ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Transportation Problem ◽  
Hash Table ◽  
Fixed Charge ◽  
Two Stage ◽  
Hybrid Heuristic ◽  
Fixed Charge Transportation Problem ◽  
Hybrid Heuristic Algorithm ◽  
Benchmark Instances ◽  
Local Search Procedure

This paper is focusing on an important transportation application encountered in supply chains, namely the capacitated two-stage fixed-charge transportation problem. For solving this complex optimization problem we described a novel hybrid heuristic approach obtained by combining a genetic algorithm based on a hash table coding of the individuals with a powerful local search procedure. The proposed algorithm was implemented and tested on an often used collection of benchmark instances and the computational results obtained showed that our proposed hybrid heuristic algorithm delivered competitive results compared to the state-of-the-art algorithms for solving the considered capacitated two-stage fixed-charge transportation problem.

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