A survey on new generation metaheuristic algorithms

In this paper we are concerned with global optimization, which can be defined as the problem of finding points on a bounded subset of Rn in which some real valued functionf assumes its optimal (i.e. maximal or minimal) value. We present a stochastic approach which is based on the simulated annealing algorithm. The approach closely follows the formulation of the simulated annealing algorithm as originally given for discrete optimization problems. The mathematic formulation is extended to continuous optimization problems and we prove asymptotic convergence to the set of global optima. Furthermore, we discuss an implementation of the algorithm and compare its performance with other well known algorithms. The performance evaluation is carried out for a standard set of test functions from the literature. Keywords: global optimization, continuous variables, simulated annealing.

Global optimization and simulated annealing

Parameters identification of photovoltaic models using an improved JAYA optimization algorithm

Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation

Time-series forecasting is an important research and application area. Much effort has been devoted over the past several decades to develop and improve the time-series forecasting models. This paper introduces a new time-series forecasting model based on the flexible neural tree (FNT). The FNT model is generated initially as a flexible multi-layer feed-forward neural network and evolved using an evolutionary procedure. Very often it is a difficult task to select the proper input variables or time-lags for constructing a time-series model. Our research demonstrates that the FNT model is capable of handing the task automatically. The performance and effectiveness of the proposed method are evaluated using time series prediction problems and compared with those of related methods.

Time-series forecasting using flexible neural tree model

Multi-objective optimization using teaching-learning-based optimization algorithm

Teaching–learning-based optimization with dynamic group strategy for global optimization

A survey of teaching–learning-based optimization

The learning experience of some other learners is introduced into TLBO so as to improve its performanceWe design a random learning strategy whatever in the Learner Phase or in the Teacher Phase18 benchmark functions and two real-world problems are used in experimental studyThe results indicate that the proposed algorithm has shown interesting outcomes To improve the global performance of the standard teaching-learning-based optimization (TLBO) algorithm, an improved TLBO algorithm (LETLBO) with learning experience of other learners is proposed in the paper In LETLBO, two random possibilities are used to determine the learning methods of learners in different phases In the Teacher Phase, the learners improve their grades by utilizing the mean information of the class and the learning experience of other learners according to a random probability In Learner Phase, the learner learns knowledge from another learner which is randomly selected from the whole class or the mutual learning experience of two randomly selected learners according to a random probability Moreover, area copying operator which is used in Producer-Scrounger model is used for parts of learners to increase its learning speed The feasibility and effectiveness of the proposed algorithm are tested on 18 benchmark functions and two practical optimization problems The merits of the improved method are compared with those of some other evolutionary algorithms (EAs), the results show that the proposed algorithm is an effective method for global optimization problems

Debao Chen

Papers

Multi-objective optimization using teaching-learning-based optimization algorithm

Teaching–learning-based optimization with dynamic group strategy for global optimization

A survey of teaching–learning-based optimization

Teaching-learning-based optimization with learning experience of other learners and its application

An improved teaching-learning-based optimization algorithm for solving global optimization problem