Influence of Initialization on the Performance of Metaheuristic Optimizers

03/08/2020
by   Qian Li, et al.
0

All metaheuristic optimization algorithms require some initialization, and the initialization for such optimizers is usually carried out randomly. However, initialization can have some significant influence on the performance of such algorithms. This paper presents a systematic comparison of 22 different initialization methods on the convergence and accuracy of five optimizers: differential evolution (DE), particle swarm optimization (PSO), cuckoo search (CS), artificial bee colony (ABC) algorithm and genetic algorithm (GA). We have used 19 different test functions with different properties and modalities to compare the possible effects of initialization, population sizes and the numbers of iterations. Rigorous statistical ranking tests indicate that 43.37% of the functions using the DE algorithm show significant differences for different initialization methods, while 73.68% of the functions using both PSO and CS algorithms are significantly affected by different initialization methods. The simulations show that DE is less sensitive to initialization, while both PSO and CS are more sensitive to initialization. In addition, under the condition of the same maximum number of function evaluations (FEs), the population size can also have a strong effect. Particle swarm optimization usually requires a larger population, while the cuckoo search needs only a small population size. Differential evolution depends more heavily on the number of iterations, a relatively small population with more iterations can lead to better results. Furthermore, ABC is more sensitive to initialization, while such initialization has little effect on GA. Some probability distributions such as the beta distribution, exponential distribution and Rayleigh distribution can usually lead to better performance. The implications of this study and further research topics are also discussed in detail.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/24/2020

On the Performance of Metaheuristics: A Different Perspective

Nowadays, we are immersed in tens of newly-proposed evolutionary and swa...
research
12/19/2013

Flower Pollination Algorithm for Global Optimization

Flower pollination is an intriguing process in the natural world. Its ev...
research
06/02/2021

Ebola Optimization Search Algorithm (EOSA): A new metaheuristic algorithm based on the propagation model of Ebola virus disease

The Ebola virus and the disease in effect tend to randomly move individu...
research
05/07/2019

Optimal Randomness in Swarm-based Search

Swarm-based search has been a hot topic for a long time. Among all the p...
research
11/30/2020

A Comparative Evaluation of Population-based Optimization Algorithms for Workflow Scheduling in Cloud-Fog Environments

This work presents a comparative evaluation of four population-based opt...
research
03/29/2021

Hybrid Evolutionary Optimization Approach for Oilfield Well Control Optimization

Oilfield production optimization is challenging due to subsurface model ...
research
02/09/2023

Do Random and Chaotic Sequences Really Cause Different PSO Performance?

Our topic is performance differences between using random and chaos for ...

Please sign up or login with your details

Forgot password? Click here to reset