Biological invasion–inspired migration in distributed evolutionary algorithms

Swarm and Evolutionary Computation

Neri

2017

Solutions to real-world problems often require the simultaneous optimisation of multiple conflicting objectives. In the presence of four or more objectives, the problem is referred to as a "many-objective optimisation problem". A problem of this category introduces many challenges, one of which is the e↵ective and e cient selection of optimal solutions.The hypervolume indicator (or s-metric), i.e. the size of dominated objective space, is an e↵ective selection criterion for many-objective optimisation. The indicator is used to measure the quality of a nondominated set, and can be used to sort solutions for selection as part of the contributing hypervolume indicator. However, hypervolume based selection methods can have a very high, if not infeasible, computational cost.The present study proposes a novel hypervolume driven selection mechanism for many-objective problems, whilst maintaining a feasible computational cost. This approach, named the Hypervolume Adaptive Grid Algorithm (HAGA), uses two-phases (narrow and broad) to prevent population-wide calculation of the contributing hypervolume indicator. Instead, HAGA only calculates the contributing hypervolume indicator for grid populations, i.e. for a few solutions, which are close in proximity (in the objective space) to a candidate solution when in competition for survival. The result is a trade-o↵ between complete accuracy in selecting the fittest individuals in regards to hypervolume quality, and a feasible computational time in many-objective space. The real-world e ciency of the proposed selection mechanism is demonstrated within the optimisation of a classifier for concealed weapon detection.

Section: Sample Size Su Ciencymentioning

confidence: 99%

A fast hypervolume driven selection mechanism for many-objective optimisation problems

Swarm and Evolutionary Computation

Neri

2017

“…The sample size of 25, in order to reduce stochastic noise, is reoccurring in the evolutionary computation literature (e.g. Yang et al 2008;Zamuda et al 2007;Falco et al 2012;García et al 2009;Wang et al 2011). To prove the sufficiency of this sample size, a large number of hypervolume indicator value samples have been produced by executing m-CMA-PAES 200 times on the DTLZ1 synthetic test problem.…”

Section: Sample Size Sufficiencymentioning

confidence: 99%

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

Shenfield

2016

Soft Comput

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 nonelitist 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) 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.

“…This sample size has been selected from evolutionary computation literature e.g. [7,38,39]. The sufficiency of this sample size was tested by producing a large number of hypervolume indicator value samples by executing WZ-MOEA/D 200 times (the distribution of which has been illustrated in Figure 15) on the WFG6 synthetic test problem using the method of performance assessment described in this section.…”

Section: Performance Assessmentmentioning

confidence: 99%

A novel preference articulation operator for the Evolutionary Multi-Objective Optimisation of classifiers in concealed weapons detection

O'Reilly

Shenfield

et al. 2015

Information Sciences

This document is the author deposited version. You are advised to consult the publisher's version if you wish to cite from it. Published versionROSTAMI, Shahin, O'REILLY, Dean, SHENFIELD, Alex and BOWRING, Nicholas (2015). A novel preference articulation operator for the Evolutionary Multi-Objective Optimisation of classifiers in concealed weapons detection. Information Sciences, 295, 494-520. Copyright and re-use policy AbstractThe incorporation of decision maker preferences is often neglected in the Evolutionary MultiObjective Optimisation (EMO) literature. The majority of the research in the field and the development of EMO algorithms is primarily focussed on converging to a Pareto optimal approximation close to or along the true Pareto front of synthetic test problems. However, when EMO is applied to real-world optimisation problems there is often a decision maker who is only interested in a portion of the Pareto front (the Region of Interest) which is defined by their expressed preferences for the problem objectives. In this paper a novel preference articulation operator for EMO algorithms is introduced (named the Weighted Z-score Preference Articulation Operator) with the flexibility of being incorporated a priori, a posteriori or progressively, and as either a primary or auxiliary fitness operator. The Weighted Z-score Preference Articulation Operator is incorporated into an implementation of the Multi-Objective Evolutionary Algorithm Based on Decomposition (named WZ-MOEA/D) and benchmarked against MOEA/D-DRA on a number of bi-objective and five-objective test problems with test cases containing preference information. After promising results are obtained when comparing WZ-MOEA/D to MOEA/D-DRA in the presence of decision maker preferences, WZ-MOEA/D is successfully applied to a real-world optimisation problem to optimise a classifier for Concealed Weapon Detection, producing better results than previously published classifier implementations.