Improved Monarch Butterfly Optimization Algorithm Based on Opposition‐Based Learning and Random Local Perturbation

Abstract

Many optimization problems have become increasingly complex, which promotes researches on the improvement of different optimization algorithms. The monarch butterfly optimization (MBO) algorithm has proven to be an effective tool to solve various kinds of optimization problems. However, in the basic MBO algorithm, the search strategy easily falls into local optima, causing premature convergence and poor performance on many complex optimization problems. To solve the issues, this paper develops a novel MBO algorithm based on opposition-based learning (OBL) and random local perturbation (RLP). Firstly, the OBL method is introduced to generate the opposition-based population coming from the original population. By comparing the opposition-based population with the original population, the better individuals are selected and pass to the next generation, and then this process can efficiently prevent the MBO from falling into a local optimum. Secondly, a new RLP is defined and introduced to improve the migration operator. This operation shares the information of excellent individuals and is helpful for guiding some poor individuals toward the optimal solution. A greedy strategy is employed to replace the elitist strategy to eliminate setting the elitist parameter in the basic MBO, and it can reduce a sorting operation and enhance the computational efficiency. Finally, an OBL and RLP-based improved MBO (OPMBO) algorithm with its complexity analysis is developed, following on which many experiments on a series of different dimensional benchmark functions are performed and the OPMBO is applied to clustering optimization on several public data sets. Experimental results demonstrate that the proposed algorithm can achieve the great optimization performance compared with a few state-of-the-art algorithms in most of the test cases.

Efficient Outlier Detection for High Dimensional Data using Improved Monarch Butterfly Optimization and Mutual Nearest Neighbors Algorithm: IMBO-MNN

“…A novel MBO algorithm based on random local perturbation and opposition-based learning to create the opposition based population from the original population and also to improve the migration operator. This MBO algorithm easily fall into the local optima is the main disadvantage [25]. From the literature survey, they mainly focused on the identification of the outlier of high dimension of data and managing the outlier with single attribute information.…”

Stochastic Economic Dispatch of Hydro-Thermal-Wind-Photovoltaic Power System Considering Mixed Coal-Blending Combustion

“…Recently, it seemed that the Monarch Butterfly Optimization (MBO) algorithm might find the enhanced function values on most of the benchmark problems with comparison to the other five algorithms [21]. Therefore, it has been applied to lots of practical optimization problems, showing an excellent performance as a new metaheuristic optimization algorithm [22][23][24][25][26].…”

Recognizing Speech Signal-to-Text Using Deep Neural Network and Opposition Based Monarch Butterfly Optimization (Dnn-Ombo)

“…Lin Sun et al [11] have industrialized a new MBO procedure entered on opposition-based learning (OBL) and random local perturbation (RLP). Yanhong Feng [12] offered opposition-based knowledge and displayed its expediency in precise developmental issues.…”