An Adaptive Chaotic Sine Cosine Algorithm for Constrained and Unconstrained Optimization
نویسندگان
چکیده
منابع مشابه
HYBRID COLLIDING BODIES OPTIMIZATION AND SINE COSINE ALGORITHM FOR OPTIMUM DESIGN OF STRUCTURES
Colliding Bodies Optimization (CBO) is a population-based metaheuristic algorithm that complies physics laws of momentum and energy. Due to the stagnation susceptibility of CBO by premature convergence and falling into local optima, some meritorious methodologies based on Sine Cosine Algorithm and a mutation operator were considered to mitigate the shortcomings mentioned earlier. Sine Cosine Al...
متن کاملModified Sine-Cosine Algorithm for Sizing Optimization of Truss Structures with Discrete Design Variables
This paper proposes a modified sine cosine algorithm (MSCA) for discrete sizing optimization of truss structures. The original sine cosine algorithm (SCA) is a population-based metaheuristic that fluctuates the search agents about the best solution based on sine and cosine functions. The efficiency of the original SCA in solving standard optimization problems of well-known mathematical function...
متن کاملAn Efficient Conjugate Gradient Algorithm for Unconstrained Optimization Problems
In this paper, an efficient conjugate gradient method for unconstrained optimization is introduced. Parameters of the method are obtained by solving an optimization problem, and using a variant of the modified secant condition. The new conjugate gradient parameter benefits from function information as well as gradient information in each iteration. The proposed method has global convergence und...
متن کاملAdaptive Cuckoo Search Algorithm for Unconstrained Optimization
Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where t...
متن کاملAn Adaptive Penalty Approach for Constrained Genetic-Algorithm Optimization
In this paper we describe a new adaptive penalty approach for handling constraints in genetic algorithm optimization problems. The idea is to start with a relatively small penalty coefficient and then increase it or decrease it on demand as the optimization progresses. Empirical results in several engineering design domains demonstrate the merit of the proposed approach.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Complexity
سال: 2020
ISSN: 1099-0526,1076-2787
DOI: 10.1155/2020/6084917