Automated parameter tuning as a bilevel optimization problem solved by a surrogate-assisted population-based approach

2021 ◽  
Author(s):  
Jesús-Adolfo Mejía-de-Dios ◽  
Efrén Mezura-Montes ◽  
Marcela Quiroz-Castellanos
Keyword(s):  
Optimization Problem ◽  
Parameter Tuning ◽  
Bilevel Optimization ◽  
Population Based

scholarly journals Optimizing community detection in social networks using antlion and K-median

2019 ◽  
Vol 8 (4) ◽  
Author(s):  
Amany A. Naem ◽  
Neveen I. Ghali
Keyword(s):  
Social Networks ◽  
Objective Function ◽  
Community Detection ◽  
Optimization Problem ◽  
Real Life ◽  
Optimization Methods ◽  
Population Based ◽  
Detection Problem ◽  
Network Communities ◽  
Antlion Optimization

Antlion Optimization (ALO) is one of the latest population based optimization methods that proved its good performance in a variety of applications. The ALO algorithm copies the hunting mechanism of antlions to ants in nature. Community detection in social networks is conclusive to understanding the concepts of the networks. Identifying network communities can be viewed as a problem of clustering a set of nodes into communities. k-median clustering is one of the popular techniques that has been applied in clustering. The problem of clustering network can be formalized as an optimization problem where a qualitatively objective function that captures the intuition of a cluster as a set of nodes with better in ternal connectivity than external connectivity is selected to be optimized. In this paper, a mixture antlion optimization and k-median for solving the community detection problem is proposed and named as K-median Modularity ALO. Experimental results which are applied on real life networks show the ability of the mixture antlion optimization and k-median to detect successfully an optimized community structure based on putting the modularity as an objective function.

Coupled Shape and Reentry Trajectory Optimization of Entry Vehicle for Lunar Return

2012 ◽  
Vol 591-593 ◽  
pp. 2624-2627
Author(s):  
Xu Zhong Wu ◽  
Sheng Jing Tang ◽  
Jie Guo
Keyword(s):  
Trajectory Optimization ◽  
Body Shape ◽  
Optimization Problem ◽  
Population Based ◽  
Impact Theory ◽  
Peak Heat Flux ◽  
Reentry Trajectory ◽  
Reentry Trajectory Optimization ◽  
Semi Empirical ◽  
Pareto Frontiers

This paper deals with the reentry trajectory optimization problem for lunar return with consideration of entry vehicle’s fore-body shape. Three performance objectives are applied in this work: cross range, peak heat flux and total heat load. Aerothermodynamic models are based on modified Newtonian impact theory and semi-empirical correlations for convective and radiative stagnation-point heat transfer. A population based evolutionary algorithm has been executed to optimize the multidisciplinary problem. At last the numerical example showed the Pareto frontiers for spherical segment and sphere cone respectively, one of optimal trajectory designs selected from the Pareto frontiers are showed in this paper. The mission requirements are satisfied through the aerothermodynamic balance.

Parameter Settings in Particle Swarm Optimization

2017 ◽  
pp. 101-132
Author(s):  
Snehal Mohan Kamalapur ◽  
Varsha Patil
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Poor Performance ◽  
Parameter Tuning ◽  
Population Based ◽  
Parameter Control ◽  
Swarm Optimization ◽  
Study Parameter ◽  
Optimal Values ◽  
And Control

The issue of parameter setting of an algorithm is one of the most promising areas of research. Particle Swarm Optimization (PSO) is population based method. The performance of PSO is sensitive to the parameter settings. In the literature of evolutionary computation there are two types of parameter settings - parameter tuning and parameter control. Static parameter tuning may lead to poor performance as optimal values of parameters may be different at different stages of run. This leads to parameter control. This chapter has two-fold objectives to provide a comprehensive discussion on parameter settings and on parameter settings of PSO. The objectives are to study parameter tuning and control, to get the insight of PSO and impact of parameters settings for particles of PSO.

Antenna Design and Direction of Arrival Estimation in Meta-Heuristic Paradigm

2016 ◽  
Vol 7 (3) ◽  
pp. 1-18 ◽  
Author(s):  
Nilanjan Dey ◽  
Amira S. Ashour
Keyword(s):  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Hybrid Methods ◽  
Direction Of Arrival ◽  
Population Based ◽  
Antenna Design ◽  
Direction Of Arrival Estimation ◽  
Wireless System ◽  
Significant Component ◽  
General Constraints

Antennas are considered as a significant component in any wireless system. There are numerous factors and constraints that affect its design. Therefore, recently several algorithms are developed to allow the designers optimize the antenna with respect to numerous different criteria, general constraints and the desired performance characteristics. In recent years there has been an increasing attention to some novel evolutionary techniques, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Bacteria-Foraging (BF), Biogeography Based Optimization (BBO), and Differential Evolution (DE) that used for antenna optimization. The current study discussed three popular population-based meta-heuristic algorithms for optimal antenna design and direction of arrival estimation. Basically, single and multi-objective population-based meta-heuristic algorithms are included. Besides hybrid methods are highlighted. This paper reviews antenna array design optimization as well as direction of arrival optimization problem for different antennas configurations.

Optimality results for a specific bilevel optimization problem

Optimization ◽  
2011 ◽  
Vol 60 (7) ◽  
pp. 813-822 ◽  
Author(s):  
S. Dempe ◽  
N. Gadhi
Keyword(s):  
Optimization Problem ◽  
Bilevel Optimization

Anticipatory Optimization of Traffic Control

2000 ◽  
Vol 1725 (1) ◽  
pp. 109-115 ◽  
Author(s):  
Henk J. van Zuylen ◽  
Henk Taale
Keyword(s):  
Traffic Control ◽  
Optimization Problem ◽  
Bilevel Optimization ◽  
Simulation Studies ◽  
Traffic Flows ◽  
System Optimum ◽  
Time Model ◽  
Traffic Conditions ◽  
Made In ◽  
Small Disturbances

Traffic control and travelers’ behavior are two mutually influential processes with different objectives. Decisions made in traffic control influence travelers’ possibilities in choosing their preferred mode, route, and time of departure; and the choices made by travelers influence the optimization possibilities for traffic control. This research presents the results of simulation studies and a mathematical analysis of this bilevel optimization problem. Under certain conditions, multiple stable situations are possible, but some of these situations are sensitive to small disturbances by which the system moves away from the original equilibrium state. There appears to be a nonlinear relationship between system parameters and the character and location of the equilibrium situations. The details of the travel time model appear to have a large influence. If road authorities want to optimize traffic control, they have to anticipate the reaction of travelers. This makes the optimization process much more complicated. Iterative optimization, where traffic control is adjusted as soon as traffic conditions change, generally does not lead to a system optimum. Methods are therefore necessary that allow for the optimization of traffic control while taking into account that traffic flows will change as a result of traffic control.

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