16 papers · ranked by Valyu relevance
Mohd Nadhir Ab Wahab, Samia Nefti-Meziani, Adham Atyabi, Catalin Buiu
'Catalin Buiu'] Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known…
Zhe Zhang, Xiaobiao Huang, Minghao Song
Optimization algorithms/techniques such as genetic algorithm, particle swarm optimization, and Gaussian process have been widely used in the accelerator field to tackle complex design/online optimization problems. However, connecting the algorithm with the optimization problem can be difficult, as the algorithms and…
Melih Peker, Ozcan Ozturk
Selecting a good set of optimization flags requires extensive effort and expert input. While most of the prior research considers using static, spatial, or dynamic features, some of the latest research directly applied deep neural networks to source code. We combined the static features, spatial features, and deep…
Florian Häse, Loïc M. Roch, Alán Aspuru-Guzik
Chimera enables multi-target optimization for experimentation or expensive computations, where evaluations are the limiting factor.
Tong Yue, Tao Li, Heming Jia, Laith Abualigah + 1 more
Global optimization problems, prevalent across scientific and engineering disciplines, necessitate efficient algorithms for navigating complex, high-dimensional search spaces. Drawing inspiration from the resilient and adaptive growth strategies of moss colonies, the moss growth optimization (MGO) algorithm presents a…
Jose A Egea, David Henriques, Thomas Cokelaer, Alejandro F Villaverde + 4 more
'Alejandro F Villaverde' 'Aidan MacNamara' 'Diana-Patricia Danciu' 'Julio R Banga' 'Julio Saez-Rodriguez'] Background Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most…
Clemens Kreutz
Insufficient performance of optimization-based approaches for the fitting of mathematical models is still a major bottleneck in systems biology. In this article, the reasons and methodological challenges are summarized as well as their impact in benchmark studies. Important aspects for achieving an increased level of…
Thanh Sang-To, Minh Hoang-Le, Magd Abdel Wahab, Thanh Cuong-Le
In this study, a meta-heuristic algorithm, named The Planet Optimization Algorithm (POA), inspired by Newton's gravitational law is proposed. POA simulates the motion of planets in the solar system. The Sun plays the key role in the algorithm as at the heart of search space. Two main phases, local and global search…
Ali Asghari, Mohammadhossein Mohammadi, Heming Jia
Metaheuristic algorithms are widely used to find optimal or near-optimal solutions for complex problems by taking inspiration from natural behaviors and processes. Although many different methods have been developed, a common problem in many of them is maintaining a good balance between exploration and exploitation and…
Ruyi Dong, Ran Cui, Zhennao Cai, Ali Asghar Heidari + 4 more
Kernel Search Optimization (KSO) is characterized by insufficient accuracy in local search, which makes it difficult to achieve local optimization. Therefore, this paper proposes a Large Local Search Kernel Search Optimization (LLSKSO) to enhance the local optimization ability. LLSKSO achieves the performance…
Rahib H. Abiyev, Mustafa Tunay
A novel learning algorithm for solving global numerical optimization problems is proposed. The proposed learning algorithm is intense stochastic search method which is based on evaluation and optimization of a hypercube and is called the hypercube optimization (HO) algorithm. The HO algorithm comprises the…
Jiahao Fan, Ying Li, Tan Wang, Seyedali Mirjalili
Metaheuristic optimization algorithms are one of the most effective methods for solving complex engineering problems. However, the performance of a metaheuristic algorithm is related to its exploration ability and exploitation ability. Therefore, to further improve the African vultures optimization algorithm (AVOA), a…
Martin Grambow, Christoph Laaber, Philipp Leitner, David Bermbach + 1 more
'Muhammad Aleem'] Performance problems in applications should ideally be detected as soon as they occur, i.e., directly when the causing code modification is added to the code repository. To this end, complex and cost-intensive application benchmarks or lightweight but less relevant microbenchmarks can be added to…
Léo Françoso Dal Piccol Sotto, Sebastian Mayer, Hemanth Janarthanam, Alexander Butz + 2 more
We consider optimization for different production requirements from the viewpoint of a bio-inspired framework for system flexibility that allows us to study the ability of an algorithm to transfer solutions from previous optimization tasks, which also relates to dynamic evolutionary optimization. Optimizing…
Xianxian Qi, Jianfeng Yang, Yiyang Zhang, Baonan Xiao + 1 more
'Antonio Puliafito'] Servers are the infrastructure of enterprise applications, and improving server performance under fixed hardware resources is an important issue. Conducting performance tuning at the application layer is common, but it is not systematic and requires prior knowledge of the running application. Some…
Jie Feng, Mingdong He, Lei Jin, Hui Dou + 1 more
Software systems often expose a large number of configurable parameters to satisfy diverse application requirements and deployment scenarios. Given the intricate dependencies between parameters, manually finding a well-performing configuration is a daunting task even for experienced operators. Most existing automatic…