Particle swarm optimization with neighborhood-based budget allocation

Abstract

The standard particle swarm optimization (PSO) algorithm allocates the total available budget of function evaluations equally and concurrently among the particles of the swarm. In the present work, we propose a new variant of PSO where each particle is dynamically assigned different computational budget based on the quality of its neighborhood. The main goal is to favor particles with high-quality neighborhoods by asynchronously providing them with more function evaluations than the rest. For this purpose, we define quality criteria to assess a neighborhood with respect to the information it possesses in terms of solutions' quality and diversity. Established stochastic techniques are employed for the final selection among the particles. Different variants are proposed by combining various quality criteria in a singleor multi-objective manner. The proposed approach is assessed on widely used test suites as well as on a set of real-world problems. Experimental evidence reveals the efficiency of the proposed approach and its competitiveness against other PSO-based variants as well as different established algorithms.

Retracted: Improved artificial bee colony algorithm with opposition‐based learning

“…The performance of the ABC algorithm is comprehensively analysed in [11]. The ABC algorithm is compared with other classical swarm intelligence algorithms such as differential evolution (DE) algorithm [12][13][14], particle swarm optimisation (PSO) algorithm [15,16], and genetic algorithm (GA) [17]. Experimental results show that the performance of the ABC algorithm is generally better than other similar algorithms.…”

Distributed filtering of networked dynamic systems with non-Gaussian noises over sensor networks: A survey

“…Note that the homogeneous architecture of sensor networks inevitably brings about poor fundamental limits and performance due mainly to the diversity of information in cyber-physical systems. Therefore, the effective information processing dependent on distributed, dynamical and heterogeneous multi-platform measurements is usually an indispensable step in the implementation of collaborative tasks [28,67,106]. For instance, the sequential design approach coupled with the minimum principle of Pontryagin and the Lagrange multiplier method has been employed in [106] to deal with the heterogeneity of sensors to realize the unbiasedness and optimality of distributed consensus filtering.…”

Assessment of Physical Properties of a Sea Dike Using Multichannel Analysis of Surface Waves and 3D Forward Modeling

“…where r 1 and r 2 are random variables that induce stochacity (Souravlias and Parsopoulos, 2016), c 1 is the cognitive parameter, c 2 is the social parameter and c 1 =c 2 =2 in most cases.…”