Evolutionary big optimization (BigOpt) of signals

Goh, Sim Kuan; Tan, Kay Chen; Mamun, Abdullah Al; Abbass, Hussein A.

doi:10.1109/cec.2015.7257307

Cited by 42 publications

(33 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this paper, we are going to compare the different winners of some recent LSGO competitions against a real-world problem, a big electroencephalography (EEG) data optimization [7]. This is a very interesting benchmark because it is a realworld problem, with both noisy and noiseless versions, and with instances with different number of variables, much bigger in its largest configuration than the other benchmarks.…”

Section: Introductionmentioning

confidence: 99%

“…In 2015, a new benchmark for the Big Data Competition was proposed 1 [7]. This benchmark is made up of three sub-problems, which differ only in the number of electrodes (and, therefore, in the number of variables to be optimized).…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, only the following three [7], [8], [9]. Maybe the reason for these few reference algorithms after 3 years is the absence of a publicly available implementation, as stated before.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparing Large-Scale Global Optimization Competition winners in a real-world problem

Molina

Nesterenko

LaTorre

2019

2019 IEEE Congress on Evolutionary Computation (CEC)

View full text Add to dashboard Cite

The optimization of thousands of variables, Large-Scale Global Optimization, is a research topic that is obtaining more and more attention by its applications in engineering and medical problems. In order to design evolutionary algorithms for these problems, several specific competitions have been organized, using benchmarks such as the ones proposed in CEC'2010 and CEC'2013, trying to simulate realistic features of real-world problems. Several algorithms have been proposed, some of them being very competitive on these benchmarks, especially during the last years. However, all of them were tested only on those artificial benchmarks, so there are no guarantees that they would obtain good performance in more realistic problems. In this paper, we select the best algorithms in these competitions to optimize a real-world problem, an electroencephalography (EEG) optimization problem. The new benchmark contains noisy problems and an increasing number of variables (up to 5000) compared to synthetic benchmarks (limited to 1000 variables). Results show that, although the fitness obtained by the majority of the algorithms is the same, the processing time strongly depends on the algorithm under consideration. The optimization time for a fixed number of fitness evaluations varies, in the most complex problems, from 3 hours to around 18 minutes, being MOS-2013 the fastest algorithm. However, if we focus our attention on the time needed to reach the best-known solution, SHADEILS becomes the fastest algorithm (with a maximum of three minutes). In our opinion, this should encourage researchers to continue working in more scalable and efficient algorithms for large-scale global optimization.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparing Large-Scale Global Optimization Competition winners in a real-world problem

Molina

Nesterenko

LaTorre

2019

2019 IEEE Congress on Evolutionary Computation (CEC)

View full text Add to dashboard Cite

show abstract

“…Thus, in the Optimization of Big Data 2015 Competition [1]- [4] (IEEE Congress on Evolutionary Computation), the problem abstracted from dealing with EEG signals through ICA is modeled as a big optimization problem (BigOpt).…”

Section: Introductionmentioning

confidence: 99%

A multi-agent genetic algorithm for big optimization problems

Zhang

Zhou

Jiang

et al. 2015

2015 IEEE Congress on Evolutionary Computation (CEC)

View full text Add to dashboard Cite

With the coming of big data age, the data usually present in a huge magnitude such as TB or more. These data contain both useful and useless information. Therefore, techniques which can effectively analyze these data are in urgent demand. In practice, dealing with Electroencephalographic (EEG) signals with Independent Component Analysis (ICA) approximates to a big optimization problem because it requires real-time, or at least automatic in dealing with signals. Thus, in the Optimization of Big Data 2015 Competition, the problem abstracted from dealing with EEG signals through ICA is modeled as a big optimization problem (BigOpt). Evolutionary optimization techniques have been successfully used in solving various optimization problems, and in the age of big data, they have attracted increasing attentions. Since the multi-agent genetic algorithm (MAGA) shows a good performance in solving large-scale problems, in this paper, based on the framework of MAGA, an MAGA is proposed for solving the big optimization problem, which is labeled as MAGA-BigOpt. In MAGA-BigOpt, the competition and self-learning operators are redesigned and combined with crossover and mutation operators to simulate the cooperation, competition, and learning behaviors of agents. Especially, in the self-learning operator, agents quickly find decreasing directions to improve itself with a heuristic strategy. In the experiments, the performance of MAGA-BigOpt is validated on the given benchmark problems from the Optimization of Big Data 2015 Competition, where both the data with and without noise are used. The results show that MAGA-BigOpt outperforms the baseline algorithm provided by the competition in both cases with lower computational costs.

show abstract

“…optimal solution are expected to be found for each MOP. While general MOPs have been extensively studied for many years [6], little work has been dedicated to solving the MOPs with a large number of decision variables, even though they also widely exist in real-world applications [7]- [9]. Although there is no formal definition, MOPs with more than 100 decision variables are known as large-scale MOPs [10], [11].…”

mentioning

confidence: 99%

Efficient Large-Scale Multiobjective Optimization Based on a Competitive Swarm Optimizer

Tian

Zheng

Zhang

et al. 2020

IEEE Trans. Cybern.

261

View full text Add to dashboard Cite

There exist many multi-objective optimization problems (MOPs) containing a large number of decision variables in real-world applications, which are known as large-scale MOPs. Due to the ineffectiveness of existing operators in finding optimal solutions in a huge decision space, some decision variable division based algorithms have been tailored for improving the search efficiency in solving large-scale MOPs. However, these algorithms will encounter difficulties when solving problems with complicated landscapes, as the decision variable division is likely to be inaccurate and time-consuming. In this paper, we propose a competitive swarm optimizer (CSO) based efficient search for solving large-scale MOPs. The proposed algorithm adopts a new particle updating strategy that suggests a twostage strategy to update position, which can highly improve the search efficiency. Experimental results on large-scale benchmark MOPs and an application example demonstrate the superiority of the proposed algorithm over several stateof-the-art multi-objective evolutionary algorithms, including problem transformation based algorithm, decision variable clustering based algorithm, particle swarm optimization algorithm, and estimation of distribution algorithm. Index Terms-Evolutionary multi-objective optimization, large-scale multi-objective optimization problem, competitive swarm optimizer, particle swarm optimization I. INTRODUCTION M ULTI-objective optimization problems (MOPs) are commonly seen in real-world applications [1]-[5], which are characterized by multiple objectives conflicting with each other. Due to the conflicting nature of the objectives, a set of trade-off solutions rather than a single Manuscript received- .

show abstract

Evolutionary big optimization (BigOpt) of signals

Cited by 42 publications

References 20 publications

Comparing Large-Scale Global Optimization Competition winners in a real-world problem

Comparing Large-Scale Global Optimization Competition winners in a real-world problem

A multi-agent genetic algorithm for big optimization problems

Efficient Large-Scale Multiobjective Optimization Based on a Competitive Swarm Optimizer

Contact Info

Product

Resources

About