Nonconvex global optimization problems in control theory

2000 ◽  
Vol 100 (5) ◽  
pp. 2531-2563 ◽  
Author(s):  
A. S. Matveev ◽  
V. A. Yakubovich
Keyword(s):  
Global Optimization ◽  
Control Theory ◽  
Optimization Problems ◽  
Nonconvex Global Optimization

Enumerative techniques for solving some nonconvex global optimization problems

OR Spectrum ◽  
1988 ◽  
Vol 10 (1) ◽  
pp. 29-35 ◽  
Author(s):  
P. M. Pardalos
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Nonconvex Global Optimization

Parametric approach to a class of nonconvex global optimization problems

Optimization ◽  
1988 ◽  
Vol 19 (1) ◽  
pp. 3-11 ◽  
Author(s):  
Phan Thien Thach ◽  
Hoang Tuy
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Parametric Approach ◽  
Nonconvex Global Optimization

Robust Solution of Nonconvex Global Optimization Problems

2005 ◽  
Vol 32 (2) ◽  
pp. 307-323 ◽  
Author(s):  
Hoang Tuy
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Robust Solution ◽  
Nonconvex Global Optimization

Hybrid orthogonal CMAES for solving global optimization problems

2013 ◽  
Vol 32 (4) ◽  
pp. 981-985
Author(s):  
Ya-fei HUANG ◽  
Xi-ming LIANG ◽  
Yi-xiong CHEN
Keyword(s):  
Global Optimization ◽  
Optimization Problems

Advancement of the search process of salp swarm algorithm for global optimization problems

2021 ◽  
pp. 115292
Author(s):  
Emre Çelik ◽  
Nihat Öztürk ◽  
Yogendra Arya
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Search Process ◽  
Salp Swarm Algorithm ◽  
Swarm Algorithm

scholarly journals Global optimization based on active preference learning with radial basis functions

2020 ◽  
Author(s):  
Alberto Bemporad ◽  
Dario Piga
Keyword(s):  
Global Optimization ◽  
Objective Function ◽  
Decision Maker ◽  
Optimization Problems ◽  
Global Optimum ◽  
Bayesian Optimization ◽  
Preference Learning ◽  
Global Optimizer ◽  
Radial Basis ◽  
Decision Vector

AbstractThis paper proposes a method for solving optimization problems in which the decision-maker cannot evaluate the objective function, but rather can only express a preference such as “this is better than that” between two candidate decision vectors. The algorithm described in this paper aims at reaching the global optimizer by iteratively proposing the decision maker a new comparison to make, based on actively learning a surrogate of the latent (unknown and perhaps unquantifiable) objective function from past sampled decision vectors and pairwise preferences. A radial-basis function surrogate is fit via linear or quadratic programming, satisfying if possible the preferences expressed by the decision maker on existing samples. The surrogate is used to propose a new sample of the decision vector for comparison with the current best candidate based on two possible criteria: minimize a combination of the surrogate and an inverse weighting distance function to balance between exploitation of the surrogate and exploration of the decision space, or maximize a function related to the probability that the new candidate will be preferred. Compared to active preference learning based on Bayesian optimization, we show that our approach is competitive in that, within the same number of comparisons, it usually approaches the global optimum more closely and is computationally lighter. Applications of the proposed algorithm to solve a set of benchmark global optimization problems, for multi-objective optimization, and for optimal tuning of a cost-sensitive neural network classifier for object recognition from images are described in the paper. MATLAB and a Python implementations of the algorithms described in the paper are available at http://cse.lab.imtlucca.it/~bemporad/glis.

scholarly journals A Hybrid Whale Optimization Algorithm for Global Optimization

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1477
Author(s):  
Chun-Yao Lee ◽  
Guang-Lin Zhuo
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Whale Optimization Algorithm ◽  
Initial Population ◽  
Test Functions ◽  
Benchmark Test ◽  
Thermal Exchange ◽  
Whale Optimization ◽  
Benchmark Test Functions

This paper proposes a hybrid whale optimization algorithm (WOA) that is derived from the genetic and thermal exchange optimization-based whale optimization algorithm (GWOA-TEO) to enhance global optimization capability. First, the high-quality initial population is generated to improve the performance of GWOA-TEO. Then, thermal exchange optimization (TEO) is applied to improve exploitation performance. Next, a memory is considered that can store historical best-so-far solutions, achieving higher performance without adding additional computational costs. Finally, a crossover operator based on the memory and a position update mechanism of the leading solution based on the memory are proposed to improve the exploration performance. The GWOA-TEO algorithm is then compared with five state-of-the-art optimization algorithms on CEC 2017 benchmark test functions and 8 UCI repository datasets. The statistical results of the CEC 2017 benchmark test functions show that the GWOA-TEO algorithm has good accuracy for global optimization. The classification results of 8 UCI repository datasets also show that the GWOA-TEO algorithm has competitive results with regard to comparison algorithms in recognition rate. Thus, the proposed algorithm is proven to execute excellent performance in solving optimization problems.

Dynamic sine cosine algorithm for large-scale global optimization problems

2021 ◽  
Vol 177 ◽  
pp. 114950
Author(s):  
Yu Li ◽  
Yiran Zhao ◽  
Jingsen Liu
Keyword(s):  
Global Optimization ◽  
Large Scale ◽  
Optimization Problems ◽  
Sine Cosine Algorithm

An improved elephant herding optimization for global optimization problems

2021 ◽  
Author(s):  
Harpreet Singh ◽  
Birmohan Singh ◽  
Manpreet Kaur
Keyword(s):  
Global Optimization ◽  
Optimization Problems
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