LoCost: A spatial social network algorithm for multi-objective optimisation

Abstract

Particle Swarm Optimisation (PSO) is increasingly being applied to optimisation of problems in engineering design and scientific investigation. While readily adapted to singleobjective problems, its use on multi-objective problems is hampered by the difficulty of finding effective means of guiding the swarm in the presence of multiple, competing objectives. This paper suggests a novel approach to this problem, based on an extension of the concepts of spatial social networks using a model of the behaviour of locusts and crickets. Comparison is made between neighbouring particles based on Pareto dominance, and a corresponding repulsion between particles added to previously suggested attractive forces. Computational experiments demonstrate that the new, spatial, social network optimisation algorithm can provide results comparable to a conventional MOPSO algorithm, and improved coverage of the Pareto-front.

Grasshopper Optimisation Algorithm: Theory and application

“…The next section proposes a new algorithm mimicking the behaviour of grasshopper swarms. There are a few works in the literature that have tried to simulate locust swarm [29][30][31][32][33]. However, they have mostly been created based on the PSO algorithm.…”

The effect of population density on the performance of a spatial social network algorithm for multi-objective optimisation

“…A qualitative comparison appears to show that LoCost has an appreciably greater coverage of the approximation to the Pareto-front than the MOPSO algorithm, "filling the gaps" in a number of places and extending the range of objective function values the attainment surface covers. To quantitatively evaluate the coverage of each method a performance measure, Ψ, introduced in an earlier paper [9] was again employed.…”

“…This form of the algorithm has been tested on a number of well known test functions taken from Zitzler et al [15]. It was found that the proposed algorithm, LoCost, performed quite comparably to a conventional MOPSO algorithm in terms of convergence, and achieved appreciably greater coverage of the approximation to the Pareto-front than the MOPSO algorithm, "filling the gaps" in a number of places [9].…”

“…Previously, the author has suggested a novel approach to this problem [9], based on an extension of the concepts of spatial social networks [4]. Using spatial social networks with "neighbourhoods" defined by Euclidian distance [14] and extending the variation in which particles move toward neighbouring particles that have found a good solution [1], a new algorithm was proposed, modelled on the behaviour of locusts and crickets.…”

Performance Improvement of OFDM Systems by Reducing Papr using SLM-Grasshopper Optimization

“…The distance between grasshopper and grasshopper is given as and is calculated as, and , s is termed as social force which defines the strength of grasshopper socially. A conceptual mannequin of the interactions between grasshoppers and the relief area the use of the characteristic s. It may additionally be stated that, in simplified form, this social interplay used to be the motivating pressure in some until now locust swarming models [15] . The house of grasshoppers is divided into three zones by function S. The zone are named as comfort zone, repulsion zone and enhancement zone.…”