4.6 Article

Differential Evolution without the Scale Factor and the Crossover Probability

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Interdisciplinary Applications

Discrete differential evolution metaheuristics for permutation flow shop scheduling problems

Marcia de Fatima Morais et al.

Summary: This paper proposes three optimization algorithms based on discrete differential evolution (DE) metaheuristics for permutation flow shop (PFS) scheduling problems. The performance of the algorithms is evaluated using various benchmarks, and the results show promising and competitive performance in terms of average performance values.

COMPUTERS & INDUSTRIAL ENGINEERING (2022)

Article Engineering, Multidisciplinary

Differential evolution with modified initialization scheme using chaotic oppositional based learning strategy

Mohamad Faiz Ahmad et al.

Summary: Differential evolution (DE) is a popular optimization algorithm known for its simplicity and fast convergence rate. This study introduces a new DE variant called chaotic oppositional DE (CODE), which combines the strengths of chaotic maps and oppositional-based learning strategy to improve the quality of the initial population. The performance of CODE variants using seven different chaotic maps is evaluated, and chaotic circle oppositional DE (CCODE) is found to be the best performing variant. The optimization performance of CCODE is compared with other existing algorithms, demonstrating its superiority in terms of solution accuracy and convergence speed.

ALEXANDRIA ENGINEERING JOURNAL (2022)

Proceedings Paper Materials Science, Multidisciplinary

Optimal scheduling with opposition based differential evolution optimized fixed head hydro-thermal power system

Chitralekha Jena et al.

Summary: This research uses optimization techniques based on Opposition-based Differential Evolution (ODE) to estimate the optimum hourly energy generation scheduling of a hydro-thermal system, aiming to improve the efficacy and quality of the solution.

MATERIALS TODAY-PROCEEDINGS (2022)

Article Computer Science, Artificial Intelligence

A new selection operator for differential evolution algorithm

Zhiqiang Zeng et al.

Summary: Most research on improving differential evolution algorithms has focused on mutation operator and parameter control. In this paper, a new selection operator is proposed to improve the algorithm’s performance, especially when individuals are in a state of stagnation. Experimental results have shown that the proposed selection operator significantly enhances the algorithm's performance and helps it escape local optimal values.

KNOWLEDGE-BASED SYSTEMS (2021)

Review Automation & Control Systems

Differential Evolution: A review of more than two decades of research

Bilal et al.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2020)

Proceedings Paper Energy & Fuels

Optimal planning of energy storage system using modified differential evolution algorithm

Chatchawan Sasantia et al.

5TH INTERNATIONAL CONFERENCE ON POWER AND ENERGY SYSTEMS ENGINEERING (CPESE 2018) (2019)

Article Computer Science, Artificial Intelligence

Adaptive α-stable differential evolution in numerical optimization

Tae Jong Choi et al.

NATURAL COMPUTING (2017)

Article Computer Science, Artificial Intelligence

Adaptive α-stable differential evolution in numerical optimization

Tae Jong Choi et al.

NATURAL COMPUTING (2017)

Article Thermodynamics

Economic optimization design for shell-and-tube heat exchangers by a Tsallis differential evolution

Emerson Hochsteiner de Vasconcelos Segundo et al.

APPLIED THERMAL ENGINEERING (2017)

Article Computer Science, Software Engineering

Globally convergent evolution strategies

Y. Diouane et al.

MATHEMATICAL PROGRAMMING (2015)

Article Computer Science, Artificial Intelligence

Solution of Jiles-Atherton vector hysteresis parameters estimation by modified Differential Evolution approaches

Leandro dos Santos Coelho et al.

EXPERT SYSTEMS WITH APPLICATIONS (2012)

Article Mathematics, Applied

A hybrid shuffled complex evolution approach based on differential evolution for unconstrained optimization

Viviana Cocco Mariani et al.

APPLIED MATHEMATICS AND COMPUTATION (2011)

Article Computer Science, Interdisciplinary Applications

Improved differential evolution approach based on cultural algorithm and diversity measure applied to solve economic load dispatch problems

Leandro dos Santos Coelho et al.

MATHEMATICS AND COMPUTERS IN SIMULATION (2009)

Article Engineering, Electrical & Electronic

Differential evolution strategy for constrained global optimization and application to practical engineering problems

Hong-Kyu Kim et al.

IEEE TRANSACTIONS ON MAGNETICS (2007)

Article Management

A hybrid scatter search/electromagnetism meta-heuristic for project scheduling

D Debels et al.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2006)

Article Computer Science, Interdisciplinary Applications

Population set-based global optimization algorithms:: some modifications and numerical studies

MM Ali et al.

COMPUTERS & OPERATIONS RESEARCH (2004)

Article Operations Research & Management Science

On the convergence of a population-based global optimization algorithm

SI Birbil et al.

JOURNAL OF GLOBAL OPTIMIZATION (2004)

Article Computer Science, Artificial Intelligence

An orthogonal genetic algorithm with quantization for global numerical optimization

YW Leung et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2001)