An advanced hybrid algorithm for constrained function optimization with engineering applications

2021 ◽  
Author(s):  
Pooja Verma ◽  
Raghav Prasad Parouha
Keyword(s):  
Hybrid Algorithm ◽  
Function Optimization ◽  
Engineering Applications ◽  
Constrained Function

An Evolutionary Algorithm for Black-Box Chance-Constrained Function Optimization

2013 ◽  
Vol 17 (2) ◽  
pp. 272-282
Author(s):  
Kazuyuki Masutomi ◽  
◽  
Yuichi Nagata ◽  
Isao Ono ◽  
◽  
...  
Keyword(s):  
Objective Function ◽  
Evolutionary Algorithm ◽  
Black Box ◽  
Function Optimization ◽  
Benchmark Problems ◽  
Objective Functions ◽  
Neural Network Controller ◽  
Objective Function Values ◽  
The Individual ◽  
Constrained Function

This paper presents an evolutionary algorithm for Black-Box Chance-Constrained Function Optimization (BBCCFO). BBCCFO is to minimize the expectation of the objective function under the constraints that the feasibility probability is higher than a userdefined constant in uncertain environments not given the mathematical expressions of objective functions and constraints explicitly. In BBCCFO, only objective function values of solutions and their feasibilities are available because the algebra expressions of objective functions and constraints cannot be used. In approaches to BBCCFO, a method based on an evolutionary algorithm proposed by Loughlin and Ranjithan shows relatively good performance in a realworld application, but this conventional method has a problem in that it requires many samples to obtain a good solution because it estimates the expectation of the objective function and the feasibility probability of an individual by sampling the individual plural times. In this paper, we propose a new evolutionary algorithm that estimates the expectation of the objective function and the feasibility probability of an individual by using the other individuals in the neighborhood of the individual. We show the effectiveness of the proposed method through experiments both in benchmark problems and in the problem of a inverted pendulum balancing with a neural network controller.

Model Selecting PSO-FA Hybrid for Complex Function Optimization

2021 ◽  
Vol 12 (3) ◽  
pp. 215-232
Author(s):  
Heng Xiao ◽  
Toshiharu Hatanaka
Keyword(s):  
Model Selection ◽  
Swarm Intelligence ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Complex Function ◽  
Group Behavior ◽  
Black Box ◽  
Function Optimization ◽  
Selection Strategy ◽  
Natural Group

Swarm intelligence is inspired by natural group behavior. It is one of the promising metaheuristics for black-box function optimization. Then plenty of swarm intelligence algorithms such as particle swarm optimization (PSO) and firefly algorithm (FA) have been developed. Since these swarm intelligence models have some common properties and inherent characteristics, model hybridization is expected to adjust a swarm intelligence model for the target problem instead of parameter tuning that needs some trial and error approach. This paper proposes a PSO-FA hybrid algorithm with a model selection strategy. An event-driven trigger based on the personal best update makes each individual do the model selection that focuses on the personal study process. By testing the proposed hybrid algorithm on some benchmark problems and comparing it with a simple PSO, the standard PSO 2011, FA, HFPSO to show how the proposed hybrid swarm averagely performs well in black-box optimization problems.

An Effective and Efficient Hybrid Algorithm Based on HS–IWO for Global Optimization

2017 ◽  
Vol 32 (04) ◽  
pp. 1859006 ◽  
Author(s):  
Aijia Ouyang ◽  
Shuo Peng ◽  
Xuyu Peng ◽  
Qian Wang
Keyword(s):  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Harmony Search ◽  
Complex Function ◽  
Function Optimization ◽  
Invasive Weed ◽  
Local Optima ◽  
Intelligent Optimization Algorithms ◽  
Complex Functions ◽  
Limit Solutions

Considering that the invasive weed optimization (IWO) algorithm and the harmony search (HS) algorithm are inclined to fall into local optima with low convergence precision when they are used to deal with complex function optimization problems, this paper proposes a hybrid algorithm, HS–IWO algorithm, which is combined HS algorithm and IWO algorithm. We introduce strategies such as fixing the number of seeds, reinitializing limit solutions, multi-individual global HS, parameter optimization, etc. In order to make the two algorithms take advantage of their merits, they are mixed organically in this paper. Through tests on some complex functions of benchmark, the experimental results display that the HS–IWO algorithm has the efficiency and robustness of the algorithms. It is an optimization algorithm that is highly effective and stable, especially to be applied to the optimization of complicated functions compared with other intelligent optimization algorithms.

scholarly journals A New Hybrid Algorithm Based on ABC and PSO for Function Optimization

2020 ◽  
Vol 864 ◽  
pp. 012065
Author(s):  
Chang-Feng Chen ◽  
Azlan Mohd Zain ◽  
Li-Ping Mo ◽  
Kai-Qing Zhou
Keyword(s):  
Hybrid Algorithm ◽  
Function Optimization
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