Social Network Search for Global Optimization

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.

An Enhanced Harris Hawk Optimization Algorithm for Parameter Estimation of Single, Double and Triple Diode Photovoltaic Models

“…Tables 1-A presents the results of EHHO and HHO for all functions, moreover two recent optimization algorithms. The two recent algorithms are social network search (SNS) algorithm [44] and wild horse optimizer (WHO) [45]. By applying Wilcoxon rank-sum test at the 5% significance level [46].…”

Enhanced social network search algorithm with powerful exploitation strategy for PV parameters estimation

“…The SNSA is inspired by users in social networks, where they attempt to be popular via several moods of imitation, conversation, disputation, and innovation. 41 These moods are mechanisms used to share the new views of users about a new event. Mathematical modeling and explanation of such moods are illustrated as follows.…”

“…Consequently, an enhanced social network search algorithm (ESNSA) is proposed in this study for estimating these electrical parameters considering the single, double, and triple diode models. The original SNSA 41 is motivated by users on social networking in various moods such as conversation, imitation, innovation, and disputation that used to share the new views of users about a new event. In this paper, SNSA's performance is enhanced via two modifications.…”

Performance Comparison of Recent Population-Based Metaheuristic Optimisation Algorithms in Mechanical Design Problems of Machinery Components

“…The SNS is a novel meta-heuristic algorithm that was inspired by the behaviour of users across the social network platforms [39]. Four different moods are considered in the algorithm: (i) imitation, (ii) conversation, (iii) disputation, and (v) innovation.…”

“…They are easy to implement and have the capability to bypass the local optima [32]. In recent years, a number of novel algorithms have been proposed in the literature, such as African Vultures Optimisation Algorithm (AVOA) [33], Crystal Structure Algorithm (CryStAl) [34], Human-Behaviour Based Optimisation (HBBO) [35], Gradient-Based Optimiser (GBO) [36], Gorilla Troops Optimiser (GTO) [37], Runge-Kutta optimiser (RUN) [38], Social Network Search (SNS) [39], and Sparrow Search Algorithm (SSA) [40]. However, a comparison of these newgeneration metaheuristic optimisation algorithms on their applicability and performance on various engineering design problems has not been evaluated.…”