scholarly journals Modelling of Flexible Manipulator System via Ant Colony Optimization for Endpoint Acceleration

2021 ◽  
Vol 2129 (1) ◽  
pp. 012016
Author(s):  
S S Z Nazri ◽  
M S Hadi ◽  
H M Yatim ◽  
M H Ab Talib ◽  
I Z M Darus
Keyword(s):  
Ant Colony Optimization ◽  
Confidence Level ◽  
Industrial Revolution ◽  
Active Vibration Control ◽  
Population Based ◽  
Ant Colony ◽  
Flexible Manipulator ◽  
Superior Performance ◽  
Flexible Manipulators ◽  
Diverse Range

Abstract The application of flexible manipulators has increased in recent years especially in the fourth industrial revolution. It plays a significant role in a diverse range of fields, such as construction automation, environmental applications, space engineering and many more. Due to the lightweight, lower inertia and high flexibility of flexible manipulators, undesired vibration may occur and affect the precision of operation. Therefore, development of an accurate model of the flexible manipulator was presented prior to establishing active vibration control to suppress the vibration and increase efficiency of the system. In this study, flexible manipulator system was modelled using the input and output experimental data of the endpoint acceleration. The model was developed by utilizing intelligence algorithm via ant colony optimization (ACO), commonly known as a population-based trail-following behaviour of real ants based on autoregressive with exogenous (ARX) model structure. The performance of the algorithm was validated based on three robustness methods known as lowest mean square error (MSE), correlation test within 95% confidence level and pole zero stability. The simulation results indicated that ACO accomplished superior performance by achieving lowest MSE of 2.5171×10−7 for endpoint acceleration. In addition, ACO portrayed correlation tests within 95% confidence level and great pole-zero stability.

Population Based Techniques for Solving the Student Project Allocation Problem

2020 ◽  
Vol 11 (2) ◽  
pp. 192-207 ◽  
Author(s):  
Patrick Kenekayoro ◽  
Promise Mebine ◽  
Bodouowei Godswill Zipamone
Keyword(s):  
Ant Colony Optimization ◽  
Constraint Satisfaction ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Population Based ◽  
Constraint Satisfaction Problems ◽  
Allocation Problem ◽  
Ant Colony ◽  
Optimal Solutions ◽  
Gravitational Search

The student project allocation problem is a well-known constraint satisfaction problem that involves assigning students to projects or supervisors based on a number of criteria. This study investigates the use of population-based strategies inspired from physical phenomena (gravitational search algorithm), evolutionary strategies (genetic algorithm), and swarm intelligence (ant colony optimization) to solve the Student Project Allocation problem for a case study from a real university. A population of solutions to the Student Project Allocation problem is represented as lists of integers, and the individuals in the population share information through population-based heuristics to find more optimal solutions. All three techniques produced satisfactory results and the adapted gravitational search algorithm for discrete variables will be useful for other constraint satisfaction problems. However, the ant colony optimization algorithm outperformed the genetic and gravitational search algorithms for finding optimal solutions to the student project allocation problem in this study.

scholarly journals Controlling the Balance of Exploration and Exploitation in ACO Algorithm

2018 ◽  
Vol 26 (4) ◽  
pp. 1-9 ◽  
Author(s):  
Ayad ‎ Mohammed Jabbar
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Problems ◽  
Dimensional Space ◽  
Probabilistic Method ◽  
Population Based ◽  
Ant Colony ◽  
Exploration And Exploitation ◽  
Classification Image ◽  
Shortest Route ◽  
Space Experiments

Ant colony optimization is a meta-heuristic algorithm inspired by the foraging behavior of real ant colony. The algorithm is a population-based solution employed in different optimization problems such as classification, image processing, clustering, and so on. This paper sheds the light on the side of improving the results of traveling salesman problem produced by the algorithm. The key success that produces the valuable results is due to the two important components of exploration and exploitation. Balancing both components is the foundation of controlling search within the ACO. This paper proposes to modify the main probabilistic method to overcome the drawbacks of the exploration problem and produces global optimal results in high dimensional space. Experiments on six variant of ant colony optimization indicate that the proposed work produces high-quality results in terms of shortest route.

scholarly journals Enhanced Ant Colony Optimization with Dynamic Mutation and Ad Hoc Initialization for Improving the Design of TSK-Type Fuzzy System

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Chi-Chung Chen ◽  
Yi-Ting Liu
Keyword(s):  
Convergence Rate ◽  
Ant Colony Optimization ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Ad Hoc ◽  
Early Stage ◽  
Systems Design ◽  
Population Based ◽  
Ant Colony ◽  
Dynamic Mutation

This paper proposes an enhanced ant colony optimization with dynamic mutation and ad hoc initialization, ACODM-I, for improving the accuracy of Takagi-Sugeno-Kang- (TSK-) type fuzzy systems design. Instead of the generic initialization usually used in most population-based algorithms, ACODM-I proposes an ad hoc application-specific initialization for generating the initial ant solutions to improve the accuracy of fuzzy system design. The generated initial ant solutions are iteratively improved by a new approach incorporating the dynamic mutation into the existing continuous ACO (ACOR). The introduced dynamic mutation balances the exploration ability and convergence rate by providing more diverse search directions in the early stage of optimization process. Application examples of two zero-order TSK-type fuzzy systems for dynamic plant tracking control and one first-order TSK-type fuzzy system for the prediction of the chaotic time series have been simulated to validate the proposed algorithm. Performance comparisons with ACOR and different advanced algorithms or neural-fuzzy models verify the superiority of the proposed algorithm. The effects on the design accuracy and convergence rate yielded by the proposed initialization and introduced dynamic mutation have also been discussed and verified in the simulations.

Omicron ACO. A New Ant Colony Optimization Algorithm

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Benjamın Baran ◽  
Osvaldo Gomez
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Problems ◽  
Population Based ◽  
Analytical Tool ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Ant Colonies ◽  
Combinatorial Optimization Problems ◽  
The Traveling Salesman Problem

Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ant colonies that has been successful in the resolution of hard combinatorial optimization problems like the Traveling Salesman Problem (TSP). This paper proposes the Omicron ACO (OA), a novel population-based ACO alternative originally designed as an analytical tool. To experimentally prove OA advantages, this work compares the behavior between the OA and the MMAS as a function of time in two well-known TSP problems. A simple study of the behavior of OA as a function of its parameters shows its robustness.

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