A multi-tier adaptive grid algorithm for the evolutionary multi-objective optimisation of complex problems

The multi-tier Covariance Matrix Adaptation Pareto Archived Evolution Strategy (m-CMA-PAES) is an evolutionary multi-objective optimisation (EMO) algorithm for real-valued optimisation problems. It combines a non-elitist adaptive grid based selection scheme with the efficient strategy parameter adaptation of the elitist Covariance Matrix Adaptation Evolution Strategy (CMA-ES). In the original CMA-PAES, a solution is selected as a parent for the next population using an elitist adaptive grid archiving (AGA) scheme derived from the Pareto Archived Evolution Strategy (PAES). In contrast, a multi-tiered AGA scheme to populate the archive using an adaptive grid for each level of non-dominated solutions in the considered candidate population is proposed. The new selection scheme improves the performance of the CMA-PAES as shown using benchmark functions from the ZDT, CEC09, and DTLZ test suite in a comparison against the (μ+λ) μ λ Multi-Objective Covariance Matrix Adaptation Evolution Strategy (MO-CMA-ES). In comparison with MO-CMA-ES, the experimental results show that the proposed algorithm offers up to a 69 % performance increase according to the Inverse Generational Distance (IGD) metric

A comprehensive review of swarm optimization algorithms

Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained, and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches

Optimal flight path planning for micro-UAV-based aerial imaging using the colony-based search algorithm

Micro-UAV systems used for metric purposes are highly capable of capturing relatively high-resolution, chromatically stable aerial images at low altitudes. In micro-UAV-based aerial imaging-based structure-from-motion (a-SfM) applications, the flight mission planning problem can be customised to achieve different objectives. The requirement for minimising the time spent in the air, which is crucial for energy conservation, can be achieved by designing the shortest possible flight path. Spatial resolution in the captured aerial images can be significantly preserved by maintaining the ground sampling distance (GSD) value within a 95% confidence interval throughout the flight path. Fuel efficiency can be improved by minimising the number of turning manoeuvers required to follow the flight path during the flying mission. In this paper, four distinct flight mission planning processes are delineated to enable the energy-efficient and effective implementation of aerial imaging missions, with their associated parameters optimised using the colony-based search algorithm (CSA). The obtained experimental results demonstrate that the proposed flight mission planning processes are highly successful in the energy-efficient and effective execution of aerial imaging missions

Using fast backpropagation algorithms for impulsive noise reduction from highly distorted images

A new Impulsive Noise elimination filter, which is based on fast backpropagation algorithms, is proposed in this paper. The simulation results show that the proposed filter achieves a superior performance over the other filters mentioned in this paper in the cases of being effective in noise suppression and detail preservation, especially when the noise density is high

Using Anfis with circular polygons for impulsive noise suppression from highly distorted images

In this paper, a novel approach is presented to the restoration of images corrupted by impulsive noise (IN), with a new nonlinear IN suppression filter, entitled circular polygons based adaptive-fuzzy filter (CF). The proposed filter is based on statistical impulse detection and nonlinear filtering which uses adaptive-network-based fuzzy inference system (Anfis) as a missed data interpolant over the circular polygons and provides estimates for the original intensity values of corrupted pixels. Impulse detection is realized by using the chi-square based goodness-of-fit test, which yields a decision about the impulsivity of each pixel. Extensive simulations were realized to demonstrate the capability of CF and they reveal that the proposed filter achieves a better performance than the other filters mentioned in this paper in the cases of being effective in noise suppression and detail preservation, also when the images are highly corrupted by IN. (c) 2004 Elsevier GmbH. All rights reserved

Using an adaptive neuro-fuzzy inference system-based interpolant for impulsive noise suppression from highly distorted images

A new impulsive noise (IN) suppression filter, entitled Adaptive neuro-fuzzy inference system (ANFIS)-based impulsive noise suppression Filter, which shows a high performance at the restoration of images distorted by IN, is proposed in this paper. The extensive simulation results show that the proposed filter achieves a superior performance to the other filters mentioned in this paper in the cases of being effective in noise suppression and detail preservation, especially when the noise density is very high. (C) 2004 Elsevier B.V. All rights reserved