Swarm Intelligence

El‐Sayed, Hoda S.; Belal, Mohamed; Almojel, Abdullah I.; Gaber, Jaafar

doi:10.1201/9781420035063.ch4

Cited by 1 publication

(1 citation statement)

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“…These particles explore the defined search space to look for solutions that better satisfy the objective function of the optimised problem. Each particle collaborates with the others during the search process by comparing its current position with the best position that it and the other particles in the swarm have found [16]. …”

Section: Particle Swarm Optimisationmentioning

confidence: 99%

Improving Vector Evaluated Particle Swarm Optimisation by Incorporating Nondominated Solutions

Lim

Ibrahim

Buyamin

et al. 2013

The Scientific World Journal

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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.

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Section: Particle Swarm Optimisationmentioning

confidence: 99%