scholarly journals Generalised Adaptive Harmony Search: A Comparative Analysis of Modern Harmony Search

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Jaco Fourie ◽  
Richard Green ◽  
Zong Woo Geem
Keyword(s):  
Simulated Annealing ◽  
Comparative Analysis ◽  
Heuristic Algorithms ◽  
Harmony Search ◽  
Particle Swarm ◽  
Parameter Tuning ◽  
Population Based ◽  
Particle Swarm Optimisation ◽  
Optimisation Algorithm ◽  
Multimodal Problems

Harmony search (HS) was introduced in 2001 as a heuristic population-based optimisation algorithm. Since then HS has become a popular alternative to other heuristic algorithms like simulated annealing and particle swarm optimisation. However, some flaws, like the need for parameter tuning, were identified and have been a topic of study for much research over the last 10 years. Many variants of HS were developed to address some of these flaws, and most of them have made substantial improvements. In this paper we compare the performance of three recent HS variants: exploratory harmony search, self-adaptive harmony search, and dynamic local-best harmony search. We compare the accuracy of these algorithms, using a set of well-known optimisation benchmark functions that include both unimodal and multimodal problems. Observations from this comparison led us to design a novel hybrid that combines the best attributes of these modern variants into a single optimiser called generalised adaptive harmony search.

scholarly journals Random Synchronous Asynchronous PSO – A Particle Swarm Optimization Algorithm with a New Iteration Strategy

Mekatronika ◽  
2019 ◽  
Vol 1 (2) ◽  
pp. 81-92
Author(s):  
Nor Azlina Ab. Aziz ◽  
Nor Hidayati Abd Aziz ◽  
Tasiransurini Ab Rahman ◽  
Norrima Mokhtar ◽  
Marizan Mubin
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Individual Performance ◽  
Population Based ◽  
Particle Swarm Optimisation ◽  
Swarm Optimization ◽  
Stochastic Optimisation ◽  
Optimisation Algorithm

Particle swarm optimisation (PSO) is a population-based stochastic optimisation algorithm. Traditionally the particles update sequence for PSO can be categorized into two groups, synchronous (S-PSO) or asynchronous (A-PSO) update. In S-PSO, the particles’ performances are evaluated before their velocity and position are updated, while in A-PSO, each particle’s velocity and position is updated immediately after individual performance is evaluated. In another study, a random asynchronous PSO (RA-PSO) has been proposed. In RA-PSO, particles are randomly chosen to be updated asynchronously, the randomness improves swarm’s exploration. RA-PSO belongs to the asynchronous group. In this paper, a new category; hybrid update sequence is proposed. The new update sequence exploits the advantages of synchronous, asynchronous, and random update methods. The proposed sequence is termed as, random synchronous-asynchronous PSO (RSA-PSO). RSA-PSO divides the particles into groups. The groups are subjected to random asynchronous update, while the particles within a chosen group are updated synchronously. The performance of RSA-PSO is compared with the existing S-PSO, A-PSO, and RA-PSO using CEC2014’s benchmark functions. The results show that RSA-PSO is superior to both A-PSO and RA-PSO, and as good as S-PSO

Parameter Settings in Particle Swarm Optimization

2017 ◽  
pp. 101-132
Author(s):  
Snehal Mohan Kamalapur ◽  
Varsha Patil
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Poor Performance ◽  
Parameter Tuning ◽  
Population Based ◽  
Parameter Control ◽  
Swarm Optimization ◽  
Study Parameter ◽  
Optimal Values ◽  
And Control

The issue of parameter setting of an algorithm is one of the most promising areas of research. Particle Swarm Optimization (PSO) is population based method. The performance of PSO is sensitive to the parameter settings. In the literature of evolutionary computation there are two types of parameter settings - parameter tuning and parameter control. Static parameter tuning may lead to poor performance as optimal values of parameters may be different at different stages of run. This leads to parameter control. This chapter has two-fold objectives to provide a comprehensive discussion on parameter settings and on parameter settings of PSO. The objectives are to study parameter tuning and control, to get the insight of PSO and impact of parameters settings for particles of PSO.

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