Social Network Search for Global Optimization

Siamak Talatahari,Meysam Saraee,Hadi Bayzidi

doi:10.1109/access.2021.3091495

Siamak Talatahari, Meysam Saraee + Show 1 more

Open Access

https://doi.org/10.1109/access.2021.3091495

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Abstract

In this paper, a novel metaheuristic algorithm called Social Network Search (SNS) is developed for solving optimization problems. The SNS algorithm simulates the attempts of users in social networks to gain more popularity by modeling the moods of users in expressing their opinions. These moods are named Imitation, Conversation, Disputation, and Innovation, which are real-world behaviors of users in social networks. These moods are used as optimization operators and model how users are affected and motivated to share their new views. To evaluate the performance of the SNS algorithm, two comparative studies with different properties were conducted. In the first step, 210 mathematical functions have been chosen, which include 120 fixed-dimension, 60 N-dimension, and 30 CEC 2014 problems. Seven metaheuristics are selected from the literature, and the statistical results of these methods are calculated and analyzed. Also, to provide a valid judgment about the performance of the new algorithm, four nonparametric statistical tests have been used. In the next step, the performance of the proposed algorithm is compared to some state-of-the-art algorithms in dealing with CEC 2017 problems. According to the performance of algorithms, the SNS method is capable of achieving better results compared to the other metaheuristics in 101 cases (48%) and performed the same or comparatively in dealing with the other problems.

Highlights

Optimization is a part of the nature of human works
Due to the random nature of the metaheuristic algorithms, the results obtained from one run is not sufficient to evaluate the performance of an algorithm
Each of the algorithms used in this study runs 50 times independently for each problem

Summary

Introduction

Optimization is a part of the nature of human works. Expressing the issues in the form of optimization problems and attempt to solve them is a very old task and dates back to the 4th century BC when Euclid raises the issue of maximizing the area of parallelogram inside a triangle. The establishment of the mathematical methods for solving optimization problems is contribute to the development of the calculus of variations. The gradient-based methods are one of these mathematical methods. These methods utilize the gradient of the objective function for solving the optimization problems and this property is the main drawback of these type of solvers [1]. These days, the optimization problems have become more complex in which their formulations are so difficult to be determined by the gradient-based methods. Some of the problems have an implicit objective function and the gradient cannot be calculated .

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