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Proceedings Paper
Computer Science, Artificial Intelligence
Mateusz Zaborski et al.
Summary: This paper introduces an evolutionary algorithm for continuous optimization and proposes improvements. The algorithm includes components such as sample archive, meta-model, and initialization, and uses linear, quadratic, and quadratic with interactions models. Experimental results show that the proposed algorithm outperforms other methods on the COBO BBOB benchmark.
ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2022, PT I
(2023)
Article
Computer Science, Software Engineering
Nikolaus Hansen et al.
Summary: COCO is an open-source platform for Comparing Continuous Optimizers in a black-box setting, aiming to automate the benchmarking of numerical optimization algorithms. It allows benchmarking deterministic and stochastic solvers for both single and multiobjective optimization in the same framework.
OPTIMIZATION METHODS & SOFTWARE
(2021)
Article
Computer Science, Information Systems
Maoqing Zhang et al.
Summary: This paper proposes a Many-Objective Evolutionary Algorithm with Adaptive Reference Vector (MaOEA-ARV) that can ensure both the spread and convergence of candidate solutions by dynamically adjusting reference vectors and adaptively partitioning candidate solutions into clusters. Experimental results demonstrate the effectiveness of MaOEA-ARV on test suites with up to 12 objectives.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Dawid Polap et al.
Summary: Foxes are popular around the globe, known for their unique hunting methods and habits. They are active throughout the year, using various tricks to hunt efficiently and adapt to changing environments. The Red Fox Optimization Algorithm (RFO) is a mathematical model developed for optimization purposes, showing potential advantages in comparison to other meta-heuristic algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Chemistry, Multidisciplinary
Mindaugas Luneckas et al.
Summary: Walking robots, a remarkable accomplishment in robotic history, still face challenges such as locomotion over irregular terrain and energy consumption. This paper presents algorithms to minimize energy consumption in hexapod robots, demonstrating energy savings with proper gait selection and efficiency through real-life experiments. The optimization algorithms showcased the potential for significant energy savings in robot movement.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Information Systems
Eneko Osaba et al.
Summary: Transfer Optimization is a new research area focusing on solving multiple optimization tasks simultaneously, with Evolutionary Multitasking being one effective approach. In this paper, a novel Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm (AT-MFCGA) is introduced to handle Evolutionary Multitasking environments. AT-MFCGA relies on cellular automata for knowledge exchange and can independently explain synergies among tasks. Experimental results show the superior performance of AT-MFCGA compared to other methods in solving multiple optimization tasks.
INFORMATION SCIENCES
(2021)
Article
Energy & Fuels
Renfei Luo et al.
Summary: This study introduces a new optimized model for identifying unknown variables in a Solid Oxide (SO) fuel cell, utilizing an improved metaheuristic method and the Red Fox Optimization Algorithm to minimize error values. The results showed promising verification with low error values for power and voltage output, indicating the effectiveness of the approach.
Article
Computer Science, Artificial Intelligence
Michal Okulewicz et al.
SWARM AND EVOLUTIONARY COMPUTATION
(2019)
Article
Computer Science, Artificial Intelligence
Michal Okulewicz et al.
APPLIED SOFT COMPUTING
(2017)
Article
Computer Science, Artificial Intelligence
Jingqiao Zhang et al.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2009)
Article
Computer Science, Artificial Intelligence
Janez Brest et al.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2006)
