Multi-objective genetic optimisation of GPC and SOFLC tuning parameters using a fuzzy-based ranking method

2000 ◽  
Vol 147 (3) ◽  
pp. 344-354 ◽  
Author(s):  
M. Mahfouf ◽  
M.F. Abbod ◽  
D.A. Linkens
Keyword(s):  
Ranking Method ◽  
Tuning Parameters ◽  
Multi Objective ◽  
Genetic Optimisation

Adaptive spectrum sensing for cognitive radio based on multi‐objective genetic optimisation

2013 ◽  
Vol 49 (17) ◽  
pp. 1099-1101 ◽  
Author(s):  
A. Balieiro ◽  
P. Yoshioka ◽  
K. Dias ◽  
C. Cordeiro ◽  
D. Cavalcanti
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Multi Objective ◽  
Genetic Optimisation

scholarly journals Beetle antennae search without parameter tuning (BAS-WPT) for multi-objective optimization

Filomat ◽  
2020 ◽  
Vol 34 (15) ◽  
pp. 5113-5119
Author(s):  
Xiangyuan Jiang ◽  
Shuai Li
Keyword(s):  
Optimization Problem ◽  
Experimental Studies ◽  
Parameter Tuning ◽  
Multi Objective Optimization ◽  
Constraint Violation ◽  
New Approach ◽  
Tuning Parameters ◽  
Original Algorithm ◽  
Multi Objective ◽  
Low Constraint

Beetle antennae search (BAS) is an efficient meta-heuristic algorithm inspired by foraging behaviors of beetles. This algorithm includes several parameters for tuning and the existing results are limited to solve single objective optimization. This work pushes forward the research on BAS by providing one variant that releases the tuning parameters and is able to handle multi-objective optimization. This new approach applies normalization to simplify the original algorithm and uses a penalty function to exploit infeasible solutions with low constraint violation to solve the constraint optimization problem. Extensive experimental studies are carried out and the results reveal efficacy of the proposed approach to constraint handling.

Integration of Cat Swarm Optimization and Borda Ranking Method for Solving Dynamic Multi-Objective Problems

2016 ◽  
Vol 15 (03) ◽  
pp. 1650014 ◽  
Author(s):  
Maysam Orouskhani ◽  
Mohammad Teshnehlab ◽  
Mohammad Ali Nekoui
Keyword(s):  
Population Based ◽  
Selection Method ◽  
Ranking Method ◽  
Borda Count ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Cat Swarm Optimization ◽  
Before And After ◽  
Traditional Approaches

This paper introduces a dynamic multi-objective optimization algorithm integrated of Cat swarm algorithm and Borda count ranking method. In the proposed approach, the cats of population are ranked and sorted based on Borda selection method just before and after a change. Then the cats with the worst Borda’s rank are re-initialized to improve the diversity of population. Also, a multi-objective cat swarm optimization (CSO) as a population-based evolutionary algorithm is applied to converge to optimal front. The simulation results indicate that the proposed algorithm achieves competitive results in comparison with traditional approaches.

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