scholarly journals Fuzzy Dynamic Parameter Adaptation in ACO and PSO for Designing Fuzzy Controllers: The Cases of Water Level and Temperature Control

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Fevrier Valdez ◽  
Juan Carlos Vazquez ◽  
Fernando Gaxiola
Keyword(s):  
Water Level ◽  
Temperature Control ◽  
Fuzzy Systems ◽  
Dynamic Parameter ◽  
Ant Colony ◽  
Benchmark Problems ◽  
Ant Colony System ◽  
Level Control ◽  
Novel Approach ◽  
Pheromone Trail

A novel approach applied to Particle Swarm Optimization (PSO) and Ant Colony Optimization is presented. The main contribution of this work is the use of fuzzy systems to dynamically update the parameters for the ACO and PSO algorithms. In the case of ACO, two fuzzy systems are designed for the Ant Colony System (ACS) algorithm variant. The first system adjusts the value for the pheromone evaporation parameter from the global pheromone trail update equation and the second system adjusts the values for the pheromone evaporation parameter from the local pheromone trail update equation. In the case of PSO, a fuzzy system is designed to find the values for the inertia weight parameter from the velocity equation. Fuzzy logic controllers (FLCs) are optimized with ACO and PSO, respectively, to prove the performance of the proposed approach. The particular benchmark problems considered to test the proposed methods are the water level control in a tank and temperature control in a shower. Therefore, PSO and ACO algorithms are applied in the optimization of the parameters of the FLCs. The achievement of the proposed fuzzy ACO and PSO algorithms is compared with the original results of each benchmark control problem.

TUNINGS OF PARAMETERS AND PHEROMONE UPDATE STRATEGY IN ANT COLONY OPTIMIZATION

2010 ◽  
Vol 09 (01) ◽  
pp. 73-83
Author(s):  
A. TAMILARASI
Keyword(s):  
Ant Colony Optimization ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Parameter Tuning ◽  
Ant Colony ◽  
Shop Floor ◽  
Benchmark Problems ◽  
Ant Colony System ◽  
Update Strategy

Scheduling is considered to be a major task to improve the shop-floor productivity. The job shop problem is under this category and is combinatorial in nature. Research on optimization of job shop problem is one of the most significant and promising areas of optimization. This paper presents an application of the Ant Colony Optimization meta heuristic to job shop problem. The main characteristics of this model are positive feedback and distributed computation. The settings of parameter values have more influence in solving instances of job shop problem. An algorithm is introduced to improve the basic ant colony system by using a pheromone updating strategy and also to analyze the quality of the solution for different values of the parameters. In this paper, we present statistical analysis for parameter tuning and we compare the quality of obtained solutions by the proposed method with the competing algorithms given in the literature for well known benchmark problems in job shop scheduling.

An Improved Ant Colony Optimization for the Vehicle Routing Problem in Logistics Distribution

2010 ◽  
Vol 121-122 ◽  
pp. 1006-1011
Author(s):  
Cheng Ming Qi
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Distribution Systems ◽  
Transition Probability ◽  
Ant Colony ◽  
Logistic Distribution ◽  
Benchmark Problems ◽  
Ant Colony System ◽  
Local Optimum ◽  
Routing Problem

The routing of a fleet of vehicles to service a set of customers is important in logistic distribution systems. The main objective of Vehicle routing problem (VRP) is to minimize the total required fleet size for serving all customers. Secondary objectives are to minimize the total distance traveled or to minimize the total route duration of all vehicles. In this paper, we present a hybrid ant colony System, named PACS, coupled with a pareto local search (PLS) algorithm and apply to the VRP and its variant, the VRP with Time Windows (VRPTW). The algorithm only chooses partial customers randomly to compute the transition probability and PLS can help to escape local optimum. Experiments on various aspects of the algorithm and computational results for some benchmark problems are reported. We compare our approach with some classic, powerful meta-heuristics and show that the proposed approach can obtain the better quality of the solutions.

Ant Colony Optimization with Local Search for Continuous Functions

2011 ◽  
Vol 204-210 ◽  
pp. 1135-1138
Author(s):  
Cheng Ming Qi
Keyword(s):  
Local Search ◽  
Ant Colony Optimization ◽  
Population Based ◽  
Continuous Functions ◽  
Ant Colony ◽  
Benchmark Problems ◽  
Ant Colony System ◽  
Local Optimum ◽  
Ant Colonies ◽  
Ant Algorithms

Ant algorithms are a recently developed, population-based approach which was inspired by the observation of the behavior of ant colonies. Based on the ant colony optimization idea, we present a hybrid ant colony system (ACS) coupled with a pareto local search (PLS) algorithm, named PACS, and apply to the continuous functions optimization. The ACS makes firstly variable range into grid. In local search, we use the PLS to escape local optimum. Computational results for some benchmark problems demonstrate that the proposed approach has the high search superior solution ability.

scholarly journals An Optimized Scalable Multi-ant Colony System for Multi-depot Vehicle Routing Problems Using a Reactive Multi-agent System

2021 ◽  
Vol 20 ◽  
pp. 249-259
Author(s):  
Guia Sana Sahar ◽  
Kazar Okba ◽  
Laouid Abdelkader ◽  
Yagoub Mohammed Amine ◽  
Reinhardt Euler ◽  
...  
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Ant Colony ◽  
Ant Colony System ◽  
Multi Agent System ◽  
Routing Problem ◽  
Agent Based ◽  
Multi Agent ◽  
Two Phases ◽  
Pheromone Trail

The multi-depot vehicle routing problem is a variant of the vehicle routing problem that tries to minimize the total cost of providing the service from several depots to satisfy several client demands. This paper presents a multi-ant colony system to solve the multi-depot vehicle routing problem using a reactive agent-based approach. This approach is designed to effectively solve the problem, in which each reactive agent is inspired by modeling the behavior of the ant. We define two types of reactive agents whose behavior differs in the use of two kinds of pheromone trail. In order to refer to the two phases of the execution process, i.e., the assignment phase and the routing phase, every reactive agent cooperates with others to provide a scalable solution for the overall problem. The solution of the multi-depot vehicle routing problem is beneficial and helpful for many real applications. The performance evaluation of the proposed approach is done using instances from the literature, and the results obtained demonstrate good performance when compared with other approaches

scholarly journals Differential Evolution with Shadowed and General Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Optimal Design of Fuzzy Controllers

Axioms ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 194
Author(s):  
Patricia Ochoa ◽  
Oscar Castillo ◽  
Patricia Melin ◽  
José Soria
Keyword(s):  
Differential Evolution ◽  
Fuzzy Systems ◽  
General Type ◽  
Differential Evolution Algorithm ◽  
Dynamic Parameter ◽  
Benchmark Problems ◽  
Parameter Adaptation ◽  
Different Types ◽  
Evolution Algorithm

This work is mainly focused on improving the differential evolution algorithm with the utilization of shadowed and general type 2 fuzzy systems to dynamically adapt one of the parameters of the evolutionary method. Previously, we have worked with both kinds of fuzzy systems in different types of benchmark problems and it has been found that the use of fuzzy logic in combination with the differential evolution algorithm gives good results. In some of the studies, it is clearly shown that, when compared to other algorithms, our methodology turns out to be statistically better. In this case, the mutation parameter is dynamically moved during the evolution process by using shadowed and general type-2 fuzzy systems. The main contribution of this work is the ability to determine, through experimentation in a benchmark control problem, which of the two kinds of the used fuzzy systems has better results when combined with the differential evolution algorithm. This is because there are no similar works to our proposal in which shadowed and general type 2 fuzzy systems are used and compared. Moreover, to validate the performance of both fuzzy systems, a noise level is used in the controller, which simulates the disturbances that may exist in the real world and is thus able to validate statistically if there are significant differences between shadowed and general type 2 fuzzy systems.

Water Level Control of Small-scale Hydroelectric Power Plant

2002 ◽  
Vol 122 (6) ◽  
pp. 989-994
Author(s):  
Shinichiro Endo ◽  
Masami Konishi ◽  
Hirosuke Imabayashi ◽  
Hayami Sugiyama
Keyword(s):  
Power Plant ◽  
Water Level ◽  
Hydroelectric Power Plant ◽  
Small Scale ◽  
Hydroelectric Power ◽  
Level Control
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