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.
Highlights
Many real-world tasks, which can be transferred to optimization problems, have become increasingly complex and are difficult to solve using the traditional optimization algorithms [1]
swarm intelligence optimization (SIO) algorithms have been widely employed for solving complex optimization problems
In order to improve the optimization efficiency of monarch butterfly optimization (MBO) algorithm and obtain an algorithm with strong universal applicability, this paper introduces opposition-based learning (OBL) into MBO and proposes random local perturbation (RLP) to improved basic MBO algorithm
Summary
Many real-world tasks, which can be transferred to optimization problems, have become increasingly complex and are difficult to solve using the traditional optimization algorithms [1]. The swarm intelligence optimization (SIO) algorithm is a kind of bionic random method inspired by natural phenomena and biological behaviors and can deal with certain high-dimensional complex and variable optimization problems because of its better computing performance and simple model [3, 4]. The FWA has powerful global optimization capabilities to solve classification problems, but there is no direct interaction among the solutions found during the optimization process of FWA; its convergence speed is slow and the computational cost is high [11]. The monarch butterfly optimization (MBO) algorithm was first presented by Wang et al [29], and it simulates the migration behaviors of monarch butterflies in nature Most of these heuristic techniques have the ability to provide fast and efficient solution, sometimes they suffer from discovering global optimal solution, slow convergence rate, and several parameters tuning [30]. The MBO algorithm and its versions have been widely used in many fields, such as dynamic vehicle routing problem [30], 0-1 knapsack problem [31], neural network straining [32], optimal power flow problem [33], and prevention of osteoporosis [34]
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