Improving Vector Evaluated Particle Swarm Optimisation by Incorporating Nondominated Solutions

Abstract

The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at the objective function optimised by that swarm, yielding poor solutions for the multiobjective optimisation problems. Thus, an improved Vector Evaluated Particle Swarm Optimisation algorithm is introduced by incorporating the nondominated solutions as the guidance for a swarm rather than using the best solution from another swarm. In this paper, the performance of improved Vector Evaluated Particle Swarm Optimisation algorithm is investigated using performance measures such as the number of nondominated solutions found, the generational distance, the spread, and the hypervolume. The results suggest that the improved Vector Evaluated Particle Swarm Optimisation algorithm has impressive performance compared with the conventional Vector Evaluated Particle Swarm Optimisation algorithm.

Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization

“…The PSO algorithm was firstly introduced by Kennedy and Eberhart in 1995 [ 30 ]. The PSO algorithm has been numerously enhanced fundamentally [ 31 , 32 ] and applied in many fields [ 33 – 35 ]. Fundamentally, the PSO algorithm follows several steps as described in Algorithm 1 : (1) initialization, (2) calculation of the fitness function, (3) updating the personal best ( pbest ) for each particle and global best ( gbest ), (4) updating the particle's velocity and the particle's position, and (5) performing termination based on a stopping criterion.…”

Evaluation of Different Peak Models of Eye Blink Eeg for Signal Peak Detection Using Artificial Neural Network

“…This algorithm is based on the movement and information sharing of particles in a multi-dimensional search space. The PSO algorithm has been numerously enhanced fundamentally [26,32] and applied in many fields [4,8,19]. A pseudo code of the PSO algorithm is described in Algorithm 1.…”

Improving Vector Evaluated Particle Swarm Optimisation Using Multiple Nondominated Leaders

“…Due to this limitation, Lim et al [10] have introduced an improved VEPSO algorithm by incorporating nondominated solutions (VEPSOnds). In VEPSOnds, as specified by (8), the g Best m ( t ) is still the solution with best fitness at m -objective function but is selected from the set of nondominated solutions and not from all p Best mi ( t ) of the m -swarm $\begin{array}{c}g\text{Bes}{\text{t}}^m=\left\{X\in \mathcal{P}\hspace{0.17em}\mid \hspace{0.17em}{f}_m\left(X\right)=\min f_m\left(\forall X\in \mathcal{P}\right)\right\},\end{array}$ where X is a nondominated solution and P is the set of nondominated solutions in the archive.…”

“…However, the VEPSO suffers from performance drawback. Therefore, it is improved by redefining the selection of the guidance from nondominated solution, known as VEPSOnds [ 10 ]. Although VEPSOnds has shown better performance than conventional VEPSO, the VEPSOnds suffers from weak performance in terms of lacking solution distributions and convergence to the true Pareto front.…”