Hybrid Nature-Inspired Optimization Algorithm: Hydrozoan and Sea Turtle Foraging Algorithms for Solving Continuous Optimization Problems

Daranat Tansui,Arit Thammano

doi:10.1109/access.2020.2984023

Abstract

In this paper, we develop a hybrid optimization algorithm inspired by the reproduction processes of hydrozoans and the foraging behavior of sea turtles for solving continuous optimization problems. Our hybrid algorithm combines the exploration capability of the hydrozoan algorithm with the exploitation capability of the sea turtle foraging algorithm. Moreover, a new adaptive crossover operator was introduced and integrated into the hybrid algorithm to further enhance exploration capability. Our hybrid algorithm was evaluated and compared to the individual algorithms and 12 state-of-the-art algorithms. Results on 21 standard benchmark functions showed that our algorithm was very effective and was among the best of the group, specifically it converged faster than the individual algorithms on most functions and reached optimal or near-optimal results on all functions.

Highlights

Optimization is an applied science that determines the best values of parameters so as to minimize or maximize an objective function of a problem, subject to constraints on the variable values
Quadratic approximation invasive weed optimization (QAIWO) performed significantly better than Invasive Weed Optimization (IWO) and Genetic Algorithm (GA)
Twenty-one standard benchmark functions shown in Table 1 were used to show the performance of Hydrozoan Algorithm (HA)-Sea Turtle Foraging Algorithm (STFA)

Summary

Introduction

Optimization is an applied science that determines the best values of parameters so as to minimize or maximize an objective function of a problem, subject to constraints on the variable values.

Results

Conclusion