Social Network Search for Solving Engineering Optimization Problems.

Hadi Bayzidi,Charles-Philippe Lamarche,Siamak Talatahari,Meysam Saraee,Radu-Emil Precup

doi:10.1155/2021/8548639

Hadi Bayzidi, Charles-Philippe Lamarche + Show 3 more

Open Access

https://doi.org/10.1155/2021/8548639

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Abstract

In this paper, a new metaheuristic optimization algorithm, called social network search (SNS), is employed for solving mixed continuous/discrete engineering optimization problems. The SNS algorithm mimics the social network user's efforts to gain more popularity by modeling the decision moods in expressing their opinions. Four decision moods, including imitation, conversation, disputation, and innovation, are real-world behaviors of users in social networks. These moods are used as optimization operators that model how users are affected and motivated to share their new views. The SNS algorithm was verified with 14 benchmark engineering optimization problems and one real application in the field of remote sensing. The performance of the proposed method is compared with various algorithms to show its effectiveness over other well-known optimizers in terms of computational cost and accuracy. In most cases, the optimal solutions achieved by the SNS are better than the best solution obtained by the existing methods.

Highlights

Optimization is a part of the nature of human works, in which almost all of the human decisions go through an optimal process [1]
Optimization is embedded in the essence of the many branches of science, for example, a system with minimal energy in physics, the maximum profit in business, survival of the best organism in biology, and designing an engineering system that satisfies a set of constraints [2, 3]
Two well-known groups of these methods are mathematical and metaheuristic methods. e idea of mathematical methods can be attributed to the development of the calculus of variations [4]. ese methods employ the gradient of the objective function and constraints of the problem to find the optimal solution. e results of these methods are exact

Summary

Introduction

Optimization is a part of the nature of human works, in which almost all of the human decisions go through an optimal process [1]. Ese aims are satisfied by developing more robust algorithms that have a better ability to search the space of problems to find a better solution. Novel metaheuristic methods are developed to find the optimal solution for complex and large-scale problems in less time than previous ones, with higher accuracy. This property arises from the right balance between exploration and exploitation of the proposed algorithm. In addition to inventing novel algorithms based on natural phenomena, developing new algorithms using hybridizing the operators of the current methods or modifying them is a hot topic in the field of metaheuristic algorithms. Algorithm with chaos [43], hybrid particle swarm optimizer, ant colony strategy and harmony search scheme

Evaluation

Mood 1

Mood 2

Mood 3

Mood 4

Methods