Improved Differential Evolution with Local Search
نویسندگان
چکیده
Differential evolution (DE) is a popular meta-heuristic optimizer which has shown good performance in solving many real-life and benchmark optimization problems. However, DE usually shows slow convergence rate at the last stage of the evolution. To enhance the performance of DE, this paper proposed an improved DE variant (OLSDE) which employs opposition-based concept and local search strategy. Experimental studies on several benchmark functions demonstrate that our approach outperforms standard DE and other two improved DE algorithms.
منابع مشابه
A Differential Evolution and Spatial Distribution based Local Search for Training Fuzzy Wavelet Neural Network
Abstract Many parameter-tuning algorithms have been proposed for training Fuzzy Wavelet Neural Networks (FWNNs). Absence of appropriate structure, convergence to local optima and low speed in learning algorithms are deficiencies of FWNNs in previous studies. In this paper, a Memetic Algorithm (MA) is introduced to train FWNN for addressing aforementioned learning lacks. Differential Evolution...
متن کاملMulti-objective Differential Evolution for the Flow shop Scheduling Problem with a Modified Learning Effect
This paper proposes an effective multi-objective differential evolution algorithm (MDES) to solve a permutation flow shop scheduling problem (PFSSP) with modified Dejong's learning effect. The proposed algorithm combines the basic differential evolution (DE) with local search and borrows the selection operator from NSGA-II to improve the general performance. First the problem is encoded with a...
متن کاملControl of nonlinear systems using a hybrid APSO-BFO algorithm: An optimum design of PID controller
This paper proposes a novel hybrid algorithm namely APSO-BFO which combines merits of Bacterial Foraging Optimization (BFO) algorithm and Adaptive Particle Swarm Optimization (APSO) algorithm to determine the optimal PID parameters for control of nonlinear systems. To balance between exploration and exploitation, the proposed hybrid algorithm accomplishes global search over the whole search spa...
متن کاملControl of nonlinear systems using a hybrid APSO-BFO algorithm: An optimum design of PID controller
This paper proposes a novel hybrid algorithm namely APSO-BFO which combines merits of Bacterial Foraging Optimization (BFO) algorithm and Adaptive Particle Swarm Optimization (APSO) algorithm to determine the optimal PID parameters for control of nonlinear systems. To balance between exploration and exploitation, the proposed hybrid algorithm accomplishes global search over the whole search spa...
متن کاملEfficient Circular Array Synthesis with a Memetic Differential Evolution Algorithm
In this article, we introduce an improved optimization based technique for the synthesis of circular antenna array. The main objective is to achieve minimum side lobe levels, maximum directivity and null control for the non-uniform, planar circular antenna array. The design procedure utilizes an improved variant of a prominent and efficient metaheuristic algorithm of current interest, namely th...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2012