Lamm-Mma

Pilát, Martin; Neruda, Roman

doi:10.1145/2001858.2001905

Cited by 5 publications

(1 citation statement)

References 4 publications

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“…Krempser et al (2017) depicts a similarity-driven additional local surrogate to enhance the performance of metaheuristic algorithm for structural optimization. A local distance-based surrogate is developed by Pilát and Neruda (2011), which is employed in a Memetic Algorithm (MA) and compared with other multiobjective EAs and global surrogate-assisted algorithms. It is often a good practice to use mixed forms of the global and local surrogates since the hybrid model can speed up with the small number of objective evaluations with exploration and exploitation (Wang et al 2019).…”

Section: Related Workmentioning

confidence: 99%

A Collaborative Beetle Antennae Search Algorithm Using Memory Based Adaptive Learning

Ghosh

Martinsen

2021

Applied Artificial Intelligence

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Recently developed Beetle Antennae Search algorithm (BAS) mimics the odor sensing mechanism of the longhorn beetles. The beetles have many species and many of these are advantageous to the nature as well as the mankind. Excepting the odor sensing activity, the beetles are naturally strong insects, and some of them have storage memory for adaptive learning and showcase social behavior. These natural mechanisms make them intelligent enough to perform the routine tasks for existence. This article proposes a novel Storage (Memory) Adaptive Collaborative BAS (SACBAS) algorithm, which incorporates the memory stored adaptive learning. This helps exploit the Group Extreme Value (GEV) instead of the Individual Extreme Values in swarm for faster convergence. Further, the SACBAS uses the reference points based on non-dominated sorting to diversify the state space. To test the data-driven performance of SACBAS, the Support Vector Machine (SVM) algorithm with linear kernel is used in this study. First, the SACBAS algorithm is tested on the multi-objective ZDT and DTLZ test-suites and compared with two recent techniques, the reference points based Nondominated Sorting Genetic Algorithm (NSGA III) and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D). Second, the data-driven SACBAS is tested with realworld cases based on offline data. The proposed SACBAS is shown to handle the offline data efficiently and obtains promising results. The Friedman Test is carried out to differentiate the SACBAS from other two techniques and the Post Hoc Test confirms that the SACBAS obtains better HyperVolume indicator scores and outperforms the NSGA III and MOEA/D.

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Section: Related Workmentioning

confidence: 99%

A Collaborative Beetle Antennae Search Algorithm Using Memory Based Adaptive Learning

Ghosh

Martinsen

2021

Applied Artificial Intelligence

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An Evolutionary Strategy for Surrogate-Based Multiobjective Optimization

Pilát

Neruda

2012

2012 IEEE Congress on Evolutionary Computation

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The paper presents a surrogate-based evolutionary strategy for multiobjective optimization. The evolutionary strategy uses distance based aggregate surrogate models in two ways: as a part of memetic search and as way to pre-select individuals in order to avoid evaluation of bad individuals. The model predicts the distance of individuals to the currently known Pareto set. The newly proposed algorithm is compared to other algorithms which use similar surrogate models on a set of benchmark functions.

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Improving many-objective optimizers with aggregate meta-models

Pilát

Neruda

2011

2011 11th International Conference on Hybrid Intelligent Systems (HIS)

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In the field of multi-objective optimization there have been attempts to reduce the number of objective function evaluations by the use of surrogate models. However, in manyobjective optimization, this work still has to be done to make the optimizers more practically usable.In this paper we show, that aggregate meta-models can be used even for the many-objective optimization and that they can also improve the performance of the many-objective optimizer. Moreover, meta-models are discussed from another point of view and compared to scalarization techniques in many-objective optimization.Two algorithms using our models are compared to IBEA on a set of selected benchmark functions with 5, 10, and 15 objectives.Index Terms-Many-objective optimization, meta-models, surrogate models, evolutionary algorithms.

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Lamm-Mma

Abstract: In this paper we describe a multiobjective memetic algorithm utilizing local distance based meta-models. This algorithm is evaluated and compared to standard multiobjective evolutionary algorithms (MOEA) as well as to a similar algorithm with a global meta-model.

Cited by 5 publications

References 4 publications

A Collaborative Beetle Antennae Search Algorithm Using Memory Based Adaptive Learning

A Collaborative Beetle Antennae Search Algorithm Using Memory Based Adaptive Learning

An Evolutionary Strategy for Surrogate-Based Multiobjective Optimization

Improving many-objective optimizers with aggregate meta-models

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