A New Optimization Algorithm Based on Search and Rescue Operations

Amir Shabani,Behrouz Asgarian,Miguel A Salido,Adriana Giret,Saeed Asil Gharebaghi

doi:10.1155/2019/2482543

Abstract

In this paper, a new optimization algorithm called the search and rescue optimization algorithm (SAR) is proposed for solving single-objective continuous optimization problems. SAR is inspired by the explorations carried out by humans during search and rescue operations. The performance of SAR was evaluated on fifty-five optimization functions including a set of classic benchmark functions and a set of modern CEC 2013 benchmark functions from the literature. The obtained results were compared with twelve optimization algorithms including well-known optimization algorithms, recent variants of GA, DE, CMA-ES, and PSO, and recent metaheuristic algorithms. The Wilcoxon signed-rank test was used for some of the comparisons, and the convergence behavior of SAR was investigated. The statistical results indicated SAR is highly competitive with the compared algorithms. Also, in order to evaluate the application of SAR on real-world optimization problems, it was applied to three engineering design problems, and the results revealed that SAR is able to find more accurate solutions with fewer function evaluations in comparison with the other existing algorithms. Thus, the proposed algorithm can be considered an efficient optimization method for real-world optimization problems.

Highlights

In our world, there are many optimization problems for which different optimization algorithms are used. ese algorithms can be classified into deterministic and stochastic optimization algorithms. e deterministic algorithms always produce the same outputs for particular inputs. ese algorithms often are used as local search algorithms
In the model we proposed, the left clues’ positions are stored in the memory matrix, whereas the humans’ positions are stored in a position matrix. e dimensions of the matrix M are equal to those of the matrix X. ey are N × D matrices, where D is the dimension of the problem and N is the number of humans. e clues matrix is a matrix containing the positions of found clues. is matrix consists of two matrices X and M
Classic benchmark functions were used in test 1 and test 2, while modern benchmarks have been used in test 3

Summary

Introduction

There are many optimization problems for which different optimization algorithms are used. ese algorithms can be classified into deterministic and stochastic optimization algorithms. e deterministic algorithms always produce the same outputs for particular inputs. ese algorithms often are used as local search algorithms. Stochastic algorithms have random components and produce different outputs for particular inputs. E genetic algorithm (GA) [2, 3], particle swarm optimization (PSO) [4, 5], and ant colony optimization (ACO) [6, 7] are some of the most widely used metaheuristic algorithms Some features of these algorithms include simple implementation, flexibility, and capability for finding the local optimum. Most metaheuristic algorithms are inspired by physical or natural phenomena, i.e., animals’ movement to find food sources These algorithms are understandable and reproducible as software programs for various optimization problems. Metaheuristic algorithms can find global optimal solutions for the problems where there are many local solutions due to their random nature. ese reasons have led to extensive use of such algorithms in solving various optimization problems

Methods

Results

Conclusion