Solving Multiobjective Optimization Problems Using an Artificial Immune System

Coello, Carlos A. Coello; Cortés, Nareli Cruz

doi:10.1007/s10710-005-6164-x

Cited by 801 publications

(270 citation statements)

References 28 publications

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“…Results indicate that the proposed approach is a viable alternative to solve multi-objective optimization problems [9].…”

Section: Introductionmentioning

confidence: 93%

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The Hybrid Classification Model Thanks to Artificial Neural Network and Artificial Immune Systems for Diagnosis of Epilepsy from Electroencephalography

Arslan¹,

Işık²

2014

JACN

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“…Results indicate that the proposed approach is a viable alternative to solve multi-objective optimization problems [9].…”

Section: Introductionmentioning

confidence: 93%

“…In the study of de Coello et al (2005), proposed an algorithm based on the clonal selection principle to solve multi-objective optimization problems (either constrained or unconstrained). Results indicate that the proposed approach is a viable alternative to solve multi-objective optimization problems [9].…”

Section: Introductionmentioning

confidence: 99%

The Hybrid Classification Model Thanks to Artificial Neural Network and Artificial Immune Systems for Diagnosis of Epilepsy from Electroencephalography

Arslan¹,

Işık²

2014

JACN

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“…Inverted Generational Distance (IGD) (Coello and Cortés 2005), is a non Pareto compliant indicator that measures the smallest distance between each point in the true discretized Pareto front (F t ) and the points in a Pareto front found by an optimizer (F k ). IGD is a widely used metric, especially in the many-objective community due to its low computational cost and its ability to measure the convergence and diversity of a Pareto front approximation at the same time.…”

Section: Performance Metrics and Statistical Testsmentioning

confidence: 99%

An investigation of clustering strategies in many-objective optimization: the I-Multi algorithm as a case study

et al. 2017

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A variety of general strategies have been applied to enhance the performance of multi-objective optimization algorithms for many-objective optimization problems (those with more than three objectives). One of these strategies is to split the solutions to cover di↵erent regions (clusters) and apply an optimizer to each region with the aim of producing more diverse solutions and achieving a better distributed approximation of the Pareto front. However, the e↵ectiveness of clustering in this context depends on a number of issues, including the characteristics of the objective functions. In this paper we show how the choice of the clustering strategy can greatly influence the behavior an optimizer. We try to find a relation between the characteristics a of multi-objective optimization problem (MOP) and the e ciency of the use of a clustering type in its resolution. Using as a case study, the Iterated Multi-swarm (I-Multi), a recently introduced multi-objective particle swarm optimization (MOPSO) algorithm, we scrutinize the impact that clustering in di↵erent spaces (of variables, objectives, and a combination of both) can have on the approximations of the Pareto front. Furthermore, using two difficult function benchmarks of problems of up to 20 objectives, we evaluate the e↵ect of using di↵erent metrics for determining the similarity between the solutions during the clustering process. Our results confirm the important e↵ect of the clustering strategy on the behavior of multi-objective optimizers. Moreover, we present evidence that some problem characteristics can be used to select the most e↵ective clustering strategy, significantly improving the quality of the Pareto front approximations produced by I-Multi.

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“…The IGD was introduced in Coello Coello and Cortés (2005) as an enhancement to the generational distance metric, measuring the proximity of the approximation set to the true Pareto optimal front in objective space. The IGD can be defined as: …”

Section: Performance Assessmentmentioning

confidence: 99%

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

Rostami

Shenfield

2016

Soft Comput

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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.

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Solving Multiobjective Optimization Problems Using an Artificial Immune System

Cited by 801 publications

References 28 publications

The Hybrid Classification Model Thanks to Artificial Neural Network and Artificial Immune Systems for Diagnosis of Epilepsy from Electroencephalography

The Hybrid Classification Model Thanks to Artificial Neural Network and Artificial Immune Systems for Diagnosis of Epilepsy from Electroencephalography

An investigation of clustering strategies in many-objective optimization: the I-Multi algorithm as a case study

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

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