scholarly journals Path Planning Using a Hybrid Evolutionary Algorithm Based on Tree Structure Encoding

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Ming-Yi Ju ◽  
Siao-En Wang ◽  
Jian-Horn Guo
Keyword(s):  
Path Planning ◽  
Evolutionary Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Tree Structure ◽  
Mobile Robot Navigation ◽  
Swarm Optimization ◽  
Hybrid Evolutionary Algorithm ◽  
Dummy Node ◽  
Encoding Method ◽  
Binary Tree Structure

A hybrid evolutionary algorithm using scalable encoding method for path planning is proposed in this paper. The scalable representation is based on binary tree structure encoding. To solve the problem of hybrid genetic algorithm and particle swarm optimization, the “dummy node” is added into the binary trees to deal with the different lengths of representations. The experimental results show that the proposed hybrid method demonstrates using fewer turning points than traditional evolutionary algorithms to generate shorter collision-free paths for mobile robot navigation.

A Hybrid Evolutionary Algorithm for Offline UAV Path Planning

2020 ◽  
pp. 205-218
Author(s):  
Soheila Ghambari ◽  
Lhassane Idoumghar ◽  
Laetitia Jourdan ◽  
Julien Lepagnot
Keyword(s):  
Path Planning ◽  
Evolutionary Algorithm ◽  
Hybrid Evolutionary Algorithm

scholarly journals Implementation of new hybrid evolutionary algorithm with fuzzy logic control approach for optimization problems

2021 ◽  
Vol 6 (4 (114)) ◽  
pp. 6-14
Author(s):  
Maan Afathi
Keyword(s):  
Fuzzy Logic ◽  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Fuzzy Logic Control ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Function Optimization ◽  
Evolutionary Computations ◽  
Hybrid Evolutionary Algorithm ◽  
Logic Control

The main purpose of using the hybrid evolutionary algorithm is to reach optimal values and achieve goals that traditional methods cannot reach and because there are different evolutionary computations, each of them has different advantages and capabilities. Therefore, researchers integrate more than one algorithm into a hybrid form to increase the ability of these algorithms to perform evolutionary computation when working alone. In this paper, we propose a new algorithm for hybrid genetic algorithm (GA) and particle swarm optimization (PSO) with fuzzy logic control (FLC) approach for function optimization. Fuzzy logic is applied to switch dynamically between evolutionary algorithms, in an attempt to improve the algorithm performance. The HEF hybrid evolutionary algorithms are compared to GA, PSO, GAPSO, and PSOGA. The comparison uses a variety of measurement functions. In addition to strongly convex functions, these functions can be uniformly distributed or not, and are valuable for evaluating our approach. Iterations of 500, 1000, and 1500 were used for each function. The HEF algorithm’s efficiency was tested on four functions. The new algorithm is often the best solution, HEF accounted for 75 % of all the tests. This method is superior to conventional methods in terms of efficiency

scholarly journals A Reduced Uncertainty-Based Hybrid Evolutionary Algorithm for Solving Dynamic Shortest-Path Routing Problem

2015 ◽  
Vol 24 (05) ◽  
pp. 1550067 ◽  
Author(s):  
Huseyin Kusetogullari ◽  
Md. Haidar Sharif ◽  
Mark S. Leeson ◽  
Turgay Celik
Keyword(s):  
Evolutionary Algorithm ◽  
Shortest Path ◽  
State Of The Art ◽  
Search Space ◽  
Packet Transmission ◽  
Routing Problem ◽  
Shortest Path Routing ◽  
Hybrid Evolutionary Algorithm ◽  
Shortest Network ◽  
Encoding Method

The need of effective packet transmission to deliver advanced performance in wireless networks creates the need to find shortest network paths efficiently and quickly. This paper addresses a reduced uncertainty-based hybrid evolutionary algorithm (RUBHEA) to solve dynamic shortest path routing problem (DSPRP) effectively and rapidly. Genetic algorithm (GA) and particle swarm optimization (PSO) are integrated as a hybrid algorithm to find the best solution within the search space of dynamically changing networks. Both GA and PSO share context of individuals to reduce uncertainty in RUBHEA. Various regions of search space are explored and learned by RUBHEA. By employing a modified priority encoding method, each individual in both GA and PSO are represented as a potential solution for DSPRP. A complete statistical analysis has been performed to compare the performance of RUBHEA with various state-of-the-art algorithms. It shows that RUBHEA is considerably superior (reducing the failure rate by up to 50%) to similar approaches with increasing number of nodes encountered in the networks.

Hybrid Evolutionary Methods

2012 ◽  
pp. 191-229
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Evolutionary Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Particle Swarm ◽  
Setup Cost ◽  
Swarm Optimization ◽  
Total Path Length ◽  
Computational Speed ◽  
Unique Manner

The limitations of single algorithm approaches lead to an attempt to hybridize or fuse multiple algorithms in the hope of removing the underlying limitations. In this chapter, the authors study the evolutionary algorithms for problem solving and try to use them in a unique manner so as to get a better performance. In the first approach, they use an evolutionary algorithm for solving the problem of motion planning in a static environment. An additional factor called momentum is introduced that controls the granularity with which a robotic path is traversed to compute its fitness. By varying the momentum, the map may be treated finer or coarser. The path evolves along the generations, with each generation adding to the maximum possible complexity of the path. Along with complexity (number of turns), the authors optimize the total path length as well as the minimum distance from the obstacle in the robotic path. The requirement of evolutionary parameter individuals as well as the maximum complexity is less at the start and more at the later stages of the algorithm. Momentum is made to decrease as the algorithm proceeds. This makes the exploration vague at the start and detailed at the later stages. As an extension to the same work, in the second approach of the chapter, the authors show the manner in which a hybrid algorithm may be used in place of simple genetic algorithm for solving the problem with momentum. A Hybrid Genetic Algorithm Particle Swarm Optimization (HGAPSO) algorithm, which is a hybrid of a genetic algorithm and particle swarm optimization, is used in the same modeling scenario. In the third and last approach, the authors present a hierarchical evolutionary algorithm that operates in two hierarchies. The coarser hierarchy finds the path in a static environment consisting of the entire robotic map. The resolution of the map is reduced for computational speed. The finer hierarchy takes a section of the map and computes the path for both static and dynamic environments. Both these hierarchies carry optimization as the robot travels in the map. The static environment path gets more and more optimized along with generations. Hence, an extra setup cost is not required like other evolutionary approaches. The finer hierarchy makes the robot easily escape from the moving obstacle, almost following the path shown by the coarser hierarchy. This hierarchy extrapolates the movements of the various objects by assuming them to be moving with same speed and direction.

Hybrid Evolutionary Methods

2019 ◽  
pp. 295-336
Author(s):  
Ritu Tiwari ◽  
Anupam Shukla ◽  
Rahul Kala
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Evolutionary Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Particle Swarm ◽  
Setup Cost ◽  
Swarm Optimization ◽  
Total Path Length ◽  
Computational Speed ◽  
Unique Manner

The limitations of single algorithm approaches lead to an attempt to hybridize or fuse multiple algorithms in the hope of removing the underlying limitations. In this chapter, the authors study the evolutionary algorithms for problem solving and try to use them in a unique manner so as to get a better performance. In the first approach, they use an evolutionary algorithm for solving the problem of motion planning in a static environment. An additional factor called momentum is introduced that controls the granularity with which a robotic path is traversed to compute its fitness. By varying the momentum, the map may be treated finer or coarser. The path evolves along the generations, with each generation adding to the maximum possible complexity of the path. Along with complexity (number of turns), the authors optimize the total path length as well as the minimum distance from the obstacle in the robotic path. The requirement of evolutionary parameter individuals as well as the maximum complexity is less at the start and more at the later stages of the algorithm. Momentum is made to decrease as the algorithm proceeds. This makes the exploration vague at the start and detailed at the later stages. As an extension to the same work, in the second approach of the chapter, the authors show the manner in which a hybrid algorithm may be used in place of simple genetic algorithm for solving the problem with momentum. A Hybrid Genetic Algorithm Particle Swarm Optimization (HGAPSO) algorithm, which is a hybrid of a genetic algorithm and particle swarm optimization, is used in the same modeling scenario. In the third and last approach, the authors present a hierarchical evolutionary algorithm that operates in two hierarchies. The coarser hierarchy finds the path in a static environment consisting of the entire robotic map. The resolution of the map is reduced for computational speed. The finer hierarchy takes a section of the map and computes the path for both static and dynamic environments. Both these hierarchies carry optimization as the robot travels in the map. The static environment path gets more and more optimized along with generations. Hence, an extra setup cost is not required like other evolutionary approaches. The finer hierarchy makes the robot easily escape from the moving obstacle, almost following the path shown by the coarser hierarchy. This hierarchy extrapolates the movements of the various objects by assuming them to be moving with same speed and direction.

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