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BMR and BWR: Two simple metaphor-free optimization algorithms for solving real-life non-convex constrained and unconstrained problems

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arXiv 2024年

作者： Rao, Ravipudi Venkata Shah, Ravikumar Sardar Vallabhbhai National Institute of Technology Ichchanath Gujarat Surat395 007 India

Two simple yet powerful optimization algorithms, named the Best-Mean-Random (BMR) and Best-Worst-Random (BWR) algorithms, are developed and presented in this paper to handle both constrained and unconstrained optimization problems. These algorithms are free of metaphors and algorithm-specific parameters. The BMR algorithm is based on the best, mean, and random solutions of the population generated for solving a given problem, and the BWR algorithm is based on the best, worst, and random solutions. The performances of the proposed two algorithms are investigated by implementing them on 26 real-life nonconvex constrained optimization problems given in the Congress on Evolutionary Computation (CEC) 2020 competition, and comparisons are made with those of the other prominent optimization algorithms. The performances on 12 constrained engineering problems are also investigated, and the results are compared with those of very recent algorithms (in some cases, compared with more than 30 algorithms). Furthermore, computational experiments are conducted on 30 unconstrained standard benchmark optimization problems, including 5 recently developed benchmark problems with distinct characteristics. The results demonstrated the superior competitiveness and superiority of the proposed simple algorithms. The optimization research community may gain an advantage by adapting these algorithms to solve various constrained and unconstrained real-life optimization problems across various scientific and engineering disciplines. © 2024, CC BY.

关键词： optimization algorithms

Computationally Efficient optimization algorithms for Model Predictive Control of Linear Systems With Integer Inputs

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Computationally Efficient Optimization Algorithms for Model ...

IEEE Annual Conference on Decision and Control

作者： Petros Karamanakos Tobias Geyer Ralph Kennel Institute for Electrical Drive Systems and Power Electronics Technische Universitat Munchen 80333 Munich Germany ABB Corporate Research 5405 Baden-Dattwil Switzerland

ISBN: (纸本)9781479978878

The model predictive control problem of linear systems with integer inputs results in an integer optimization problem. In case of a quadratic objective function, the optimization problem can be cast as an integer least-squares (ILS) problem. Three algorithms to solve this problem are proposed in this paper. Optimality can be traded in to reduce the computation time. An industrial case study - an inverterdriven electrical drive system - is considered to demonstrate the effectiveness of the presented techniques.

关键词： Integer Linear system optimization algorithms Model predictive control Predictive control nonlinear model predictive control computational time

Electromagnetically Consistent optimization algorithms for the Global Design of RIS

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arXiv 2024年

作者： Shabir, M.W. Di Renzo, M. Zappone, A. Debbah, M. Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systèmes Gif-sur-Yvette France University of Cassino Cassino Italy Khalifa University Abu Dhabi United Arab Emirates

The reconfigurable intelligent surface is an emerging technology for wireless communications. We model it as an inhomogeneous boundary of surface impedance, and consider various optimization problems that offer different tradeoffs in terms of performance and implementation complexity. The considered non-convex optimization problems are reformulated as a sequence of approximating linear quadratically constrained or semidefinite programs, which are proved to have a polynomial complexity and to converge monotonically in the objective value. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

Accelerated optimization algorithms and ordinary differential equations: the convex non Euclidean case

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arXiv 2024年

作者： Dobson, Paul Sanz-Serna, Jesus María Zygalakis, Konstantinos C. Maxwell Institute for Mathematical Sciences Mathematics Department Heriot-Watt University EdinburghEH14 4AS United Kingdom Departamento de Matemáticas Universidad Carlos III de Madrid Avenida Universidad 30 Leganés Madrid28911 Spain Maxwell Institute for Mathematical Sciences School of Mathematics University of Edinburgh Peter Guthrie Tait Rd EdinburghEH9 3FD United Kingdom

We study the connections between ordinary differential equations and optimization algorithms in a non-Euclidean setting. We propose a novel accelerated algorithm for minimising convex functions over a convex constrained set. This algorithm is a natural generalization of Nesterov’s accelerated gradient descent method to the non-Euclidean setting and can be interpreted as an additive Runge-Kutta algorithm. The algorithm can also be derived as a numerical discretization of the ODE appearing in Krichene et al. (2015a). We use Lyapunov functions to establish convergence rates for the ODE and show that the discretizations considered achieve acceleration beyond the setting studied in Krichene et al. (2015a). Finally, we discuss how the proposed algorithm connects to various equations and algorithms in the *** Codes 65L06, 65L20, 90C25 Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

Enhancing State-of-the-art Multi-objective optimization algorithms by Applying Domain Specific Operators

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Enhancing State-of-the-art Multi-objective Optimization Algo...

IEEE Symposium Series on Computational Intelligence

作者： Seyyedeh Newsha Ghoreishi Jan Corfixen Sorensen Bo Norregaard Jorgensen Center for Energy Inf. Univ. of Southern Denmark Odense Denmark

ISBN: (纸本)9781479975617

To solve dynamic multi-optimization problems, optimization algorithms are required to converge quickly in response to changes in the environment without reducing the diversity of the found solutions. Most Multi-Objective Evolutionary algorithms (MOEAs) are designed to solve static multi-objective optimization problems where the environment does not change dynamically. For that reason, the requirement for convergence in static optimization problems is not as time-critical as for dynamic optimization problems. Most MOEAs use generic variables and operators that scale to static multi-objective optimization problems. Problems emerge when the algorithms can not converge fast enough, due to scalability issues introduced by using too generic operators. This paper presents an evolutionary algorithm CONTROLEUM-GA that uses domain specific variables and operators to solve a real dynamic greenhouse climate control problem. The domain specific operators only encode existing knowledge about the environment. A comprehensive comparative study is provided to evaluate the results of applying the CONTROLEUM-GA compared to NSGAII, ε-NSGAII and ε-MOEA. Experimental results demonstrate clear improvements in convergence time without compromising the quality of the found solutions compared to other state-of-art algorithms.

关键词： optimization algorithms Operators Operon Converge OPERATORS Environments Changing environments Domain evolutionary algorithm Solve

Disparity Map Estimation Based on optimization algorithms using Satellite Stereo Imagery

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Disparity Map Estimation Based on Optimization Algorithms us...

IEEE International Conference on Signal and Image Processing Applications

作者： Abdul Qayyum Aamir Saeed Malik Muhammad Naufal Bin Muhammad Saad Faris Abdullah Mahboob Iqbal Electrical and Electronics Engineering Department Universiti Teknologi PETRONAS Tronoh Perak Malaysia.

ISBN: (纸本)9781479989973

The monitoring of trees and vegetation near high voltage transmission power lines is a tedious job for electrical companies. There are many blackouts occur due to interfering the trees with the power transmission lines in hilly as well as urban areas. This is a big challenge for power distribution companies to monitor the vegetation for avoiding the blackouts and flashovers. To solve these problems, there are many methods are used to monitor the trees and vegetation near transmission power poles. But the existing methods are expensive and time consuming. We proposed the new method based on satellite images to monitor the trees and vegetation. The satellite images provide the cost effective solution to solve the monitoring problem. In this paper, we proposed the stereo matching algorithms to measure the disparity map based on satellite stereo imagery. The height estimation of trees and vegetation near power poles based on depth map which is inversely proportional of the disparity map. For measuring the depth map, the dynamic programming (DP) and block matching with energy minimization has been proposed. These techniques are applied on the satellite stereo images and based on results, our proposed DP algorithm produced more accurate disparity map as compared to the block matching algorithm.

关键词： Pleiades satellite images IKONOS Stereo matching algorithms Dynamic programming algorithm Block matching algorithm Energy minimization dynamic programming algorithm block matching vegetation optimization algorithms MAPPING(MATHEMATICAL) maps trees Trees ENERGY MINIMISATION Power transmission lines images satellites Monitoring problem ELECTRICITY POLE Blackouts

Two Criteria for Performance Analysis of optimization algorithms

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arXiv 2024年

作者： Jing, Yunpeng Liu, Hailin Liu, Qunfeng

Performance analysis is crucial in optimization research, especially when addressing black-box problems through nature-inspired algorithms. Current practices often rely heavily on statistical methods, which can lead to various logical paradoxes. To address this challenge, this paper introduces two criteria to ensure that performance analysis is unaffected by irrelevant factors. The first is the isomorphism criterion, which asserts that performance evaluation should remain unaffected by the modeling approach. The second is the IIA criterion, stating that comparisons between two algorithms should not be influenced by irrelevant third-party algorithms. Additionally, we conduct a comprehensive examination of the underlying causes of these paradoxes, identify conditions for checking the criteria, and propose ideas to tackle these issues. The criteria presented offer a framework for researchers to critically assess the performance metrics or ranking methods, ultimately aiming to enhance the rigor of evaluation metrics and ranking methods. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

Using the Empirical Attainment Function for Analyzing Single-objective Black-box optimization algorithms

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arXiv 2024年

作者： López-Ibáñez, Manuel Vermetten, Diederick Dreo, Johann Doerr, Carola Alliance Manchester Business School University of Manchester ManchesterM13 9PL United Kingdom Leiden Institute for Advanced Computer Science Leiden Netherlands Pasteur Institute Université Paris Cité Bioinformatics and Biostatistics Hub Department of Computational Biology Systems Biology Group Paris France Sorbonne Université CNRS LIP6 Paris France

A widely accepted way to assess the performance of iterative black-box optimizers is to analyze their empirical cumulative distribution function (ECDF) of pre-defined quality targets achieved not later than a given runtime. In this work, we consider an alternative approach, based on the empirical attainment function (EAF) and we show that the target-based ECDF is an approximation of the EAF. We argue that the EAF has several advantages over the target-based ECDF. In particular, it does not require defining a priori quality targets per function, captures performance differences more precisely, and enables the use of additional summary statistics that enrich the analysis. We also show that the average area over the convergence curves is a simpler-to-calculate, but equivalent, measure of anytime performance. To facilitate the accessibility of the EAF, we integrate a module to compute it into the IOHanalyzer platform. Finally, we illustrate the use of the EAF via synthetic examples and via the data available for the BBOB suite. © 2024, CC BY.

关键词： optimization algorithms

Discrete Parameter Simulation optimization algorithms with Applications to Admission Control with Dependent Service Times

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Discrete Parameter Simulation Optimization Algorithms with A...

Proceedings of the 46th IEEE Conference on Decision and Control, Pages 919-1382

作者： Vivek Mishra Shalabh Bhatnagar N. Hemachandra Department of Computer Science and Automation Indian Institute of Science Bangalore India Department of Industrial Engineering and Operations Research Indian Institute of Technology Mumbai India

ISBN: (纸本)9781424414970;1424414970

We propose certain discrete parameter variants of well known simulation optimization algorithms. Two of these algorithms are based on the smoothed functional (SF) technique while two others are based on the simultaneous perturbation stochastic approximation (SPSA) method. They differ from each other in the way perturbations are obtained and also the manner in which projections and parameter updates are performed. All our algorithms use two simulations and two-timescale stochastic approximation. As an application setting, we consider the important problem of admission control of packets in communication networks under dependent service times. We consider a discrete time slotted queueing model of the system and consider two different scenarios - one where the service times have a dependence on the system state and the other where they depend on the number of arrivals in a time slot. Under our settings, the simulated objective function appears ill-behaved with multiple local minima and a unique global minimum characterized by a sharp dip in the objective function in a small region of the parameter space. We compare the performance of our algorithms on these settings and observe that the two SF algorithms show the best results overall. In fact, in many cases studied, SF algorithms converge to the global minimum.

关键词： Admission control parameter space methods algorithms Global Minimum optimization algorithms Service time simulating Objective function stochastic approximation networks(communications) arrivals discrete-time