A test-suite of non-convex constrained optimization problems from the real-world and some baseline results
Real-world optimization problems have been comparatively difficult to solve due to the
complex nature of the objective function with a substantial number of constraints. To deal …
complex nature of the objective function with a substantial number of constraints. To deal …
Metaheuristic optimization of power and energy systems: Underlying principles and main issues of the 'rush to heuristics'
In the power and energy systems area, a progressive increase of literature contributions that
contain applications of metaheuristic algorithms is occurring. In many cases, these …
contain applications of metaheuristic algorithms is occurring. In many cases, these …
COCO: A platform for comparing continuous optimizers in a black-box setting
We introduce COCO, an open-source platform for Comparing Continuous Optimizers in a
black-box setting. COCO aims at automatizing the tedious and repetitive task of …
black-box setting. COCO aims at automatizing the tedious and repetitive task of …
Benchmarking evolutionary algorithms for single objective real-valued constrained optimization–a critical review
Benchmarking plays an important role in the development of novel search algorithms as well
as for the assessment and comparison of contemporary algorithmic ideas. This paper …
as for the assessment and comparison of contemporary algorithmic ideas. This paper …
Automated algorithm selection on continuous black-box problems by combining exploratory landscape analysis and machine learning
P Kerschke, H Trautmann - Evolutionary computation, 2019 - direct.mit.edu
In this article, we build upon previous work on designing informative and efficient
Exploratory Landscape Analysis features for characterizing problems' landscapes and show …
Exploratory Landscape Analysis features for characterizing problems' landscapes and show …
Evaluating the performance of meta-heuristic algorithms on CEC 2021 benchmark problems
To develop new meta-heuristic algorithms and evaluate on the benchmark functions is the
most challenging task. In this paper, performance of the various developed meta-heuristic …
most challenging task. In this paper, performance of the various developed meta-heuristic …
Algorithm selection based on exploratory landscape analysis and cost-sensitive learning
The steady supply of new optimization methods makes the algorithm selection problem
(ASP) an increasingly pressing and challenging task, specially for real-world black-box …
(ASP) an increasingly pressing and challenging task, specially for real-world black-box …
Swarm intelligence and evolutionary algorithms: Performance versus speed
AP Piotrowski, MJ Napiorkowski, JJ Napiorkowski… - Information …, 2017 - Elsevier
The popularity of metaheuristics, especially Swarm Intelligence and Evolutionary Algorithms,
has increased rapidly over the last two decades. Numerous algorithms are proposed each …
has increased rapidly over the last two decades. Numerous algorithms are proposed each …
[HTML][HTML] Imprecise bayesian optimization
J Rodemann, T Augustin - Knowledge-Based Systems, 2024 - Elsevier
Bayesian optimization (BO) with Gaussian processes (GPs) surrogate models is widely used
to optimize analytically unknown and expensive-to-evaluate functions. In this paper, we …
to optimize analytically unknown and expensive-to-evaluate functions. In this paper, we …
Choice of benchmark optimization problems does matter
AP Piotrowski, JJ Napiorkowski… - Swarm and Evolutionary …, 2023 - Elsevier
Various benchmark sets have already been proposed to facilitate comparison between
metaheuristics, or Evolutionary Algorithms. During the competition, typically algorithms are …
metaheuristics, or Evolutionary Algorithms. During the competition, typically algorithms are …