Static and Dynamic Multimodal Optimization by Improved Covariance Matrix Self-Adaptation Evolution Strategy With Repelling Subpopulations

Ahrari, Ali; Elsayed, Saber M.; Sarker, Ruhul A.; Essam, Daryl; Coello, Carlos A. Coello

doi:10.1109/tevc.2021.3117116

Cited by 13 publications

(6 citation statements)

References 49 publications

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“…( 8), which belong to the source task j and the target task i respectively, can reduce the difference in weights of the rank prediction processes (i.e., H s (xgb,j) and H t (xmgb,j)) of the two solutions in Eq. (9). In this way, our proposed optimization model in CTM can be regarded as minimizing the gap between the learned H s (x) and H t (x).…”

Section: ) Explanation For the Ctm Mechanismmentioning

confidence: 99%

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Orthogonal Transfer for Multitask Optimization

Zhan

Tan

et al. 2023

IEEE Trans. Evol. Computat.

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Knowledge transfer (KT) plays a key role in multitask optimization. However, most of the existing KT methods still face two challenges. First, the tasks may commonly have different dimensionalities, making the KT between heterogeneous search spaces very difficult. Second, the tasks may have different degrees of similarity in different dimensions, making that treating all dimensions with equal importance may be harmful to the KT process. To address these two challenges, this paper proposes a novel orthogonal transfer (OT) method that is enabled by a cross-task mapping (CTM) strategy, which can achieve high-quality KT among heterogeneous tasks. For the first challenge, the CTM strategy maps the global best individual of one task from its original search space to the search space of the target task via an optimization process, which can handle the difference in task dimensionality. For the second challenge, the OT method is performed on the CTM-obtained individual and a random individual of the target task to find the best combination of different dimensions in these two individuals rather than treating all the dimensions equally, so as to achieve high-quality KT. To verify the effectiveness of the proposed OT method and the resulted OT-based multitask optimization (OTMTO) algorithm, this paper not only uses the existing multitask optimization benchmark but also proposes a new benchmark test suite named multitask optimization problems with different dimensionalities. Comprehensive experimental results on the existing and the proposed benchmarks show that the proposed OT method and the OTMTO algorithm are very advantageous in providing high-quality KT and in handling the heterogeneity of search space in multitask optimization problems compared to the existing competitive evolutionary multitask optimization algorithms.

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Section: ) Explanation For the Ctm Mechanismmentioning

confidence: 99%

“…The evolutionary process executes repeatedly until a stopping criterion is met. EC algorithms have been successfully applied to complex optimization problems like large-scale [3]- [5], dynamic [6] [7], multimodal [8] [9], multi-/many-objective [10]- [12], expensive [13]- [15], and real-world applications [16]- [20].…”

Section: Introductionmentioning

confidence: 99%

Orthogonal Transfer for Multitask Optimization

Zhan

Tan

et al. 2023

IEEE Trans. Evol. Computat.

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“…Most existing optimization algorithms for UMOPs or MMOPs tends to guide particles to certain locations according to the global and local optimal locations (Figure 3(left)). Such a feature helps UMOPs or MMOPs to let particles converge to one or several locations [25,26]. However, for ROMPs such as the sampling process of LSF described above, continuity of the solutions will be ignored using such a searching pattern, leading to a poor approximation of the LSF.…”

Section: Normal Search Patternmentioning

confidence: 99%

“…The proposed NSPSO is designed to deal with RMOPs such as the finding of LSF which can also be seen as a special multimodal optimization problem. Therefore four-NMMSO [31], RSCMSAESII [25], Multi_AMO [32] and DP-MSCC-ES [33]-are chosen as competitive algorithms for the comparative study. Notably, the RS-CMSA-ESII won the championship in the GECCO 2020 Competition on Niching Methods for Multimodal Optimization.…”

Section: Comparative Analysis Settingsmentioning

confidence: 99%

A Novel Sampling Method Based on Normal Search Particle Swarm Optimization for Active Learning Reliability Analysis

Yuan

et al. 2023

Applied Sciences

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In active learning reliability methods, an approximation of limit state function (LSF) with high precision is the key to accurately calculating the failure probability (Pf). However, existing sampling methods cannot guarantee that candidate samples can approach the LSF actively, which lowers the accuracy and stability of the results and causes excess computational effort. In this paper, a novel candidate samples-generating algorithm was proposed, by which a group of evenly distributed candidate points on the predicted LSF of performance function (either the real one or the surrogate model) could be obtained. In the proposed method, determination of LSF is considered as an optimization problem in which the absolute value of performance function was considered as objective function. After this, a normal search particle swarm optimization (NSPSO) was designed to deal with such problems, which consists of a normal search pattern and a multi-strategy framework that ensures the uniform distribution and diversity of the solution that intends to cover the optimal region. Four explicit performance functions and two engineering cases were employed to verify the effectiveness and accuracy of NSPSO sampling method. Four state-of-the-art multi-modal optimization algorithms were used as competitive methods. Analysis results show that the proposed method outperformed all competitive methods and can provide candidate samples that evenly distributed on the LSF.

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“…Currently, the ANN-based learning branch has aroused great attention due to the success of deep learning (DL) in various real-world applications [5]- [7]. More significantly, EC algorithms have also made the great pace in research and applications [8]- [13]. EC algorithms were born in the 1960s, when computer scientists designed EC algorithms such as the genetic algorithm (GA) [14] [15], evolution strategy [16], and evolutionary programming [17]- [19] for solving optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Learning-Aided Evolution for Optimization

Zhan

Zhang

2023

IEEE Trans. Evol. Computat.

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Abstract-Learning and optimization are the two essential abilities of human beings for problem solving. Similarly, computer scientists have made great efforts to design artificial neural network (ANN) and evolutionary computation (EC) to simulate the learning ability and the optimization ability for solving real-world problems, respectively. These have been two essential branches in artificial intelligence (AI) and computer science. However, in humans, learning and optimization are usually integrated together for problem solving. Therefore, how to efficiently integrate these two abilities together to develop powerful AI remains a significant but challenging issue. Motivated by this, this paper proposes a novel learning-aided evolutionary optimization (LEO) framework that plus learning and evolution for solving optimization problems. The LEO is integrated with the evolution knowledge learned by ANN from the evolution process of EC to promote optimization efficiency. The LEO framework is applied to both classical EC algorithms and some state-of-the-art EC algorithms including a champion algorithm, with benchmarking against the IEEE Congress on Evolutionary Computation competition data. The experimental results show that the LEO can significantly enhance the existing EC algorithms to better solve both single-objective and multi-/many-objective global optimization problems, suggesting that learning plus evolution is more intelligent for problem solving. Moreover, the experimental results have also validated the time efficiency of the LEO, where the additional time cost for using LEO is greatly deserved. Therefore, the promising LEO can lead to a new and more efficient paradigm for EC algorithms to solve global optimization problems by plus learning and evolution.

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Static and Dynamic Multimodal Optimization by Improved Covariance Matrix Self-Adaptation Evolution Strategy With Repelling Subpopulations

Cited by 13 publications

References 49 publications

Orthogonal Transfer for Multitask Optimization

Orthogonal Transfer for Multitask Optimization

A Novel Sampling Method Based on Normal Search Particle Swarm Optimization for Active Learning Reliability Analysis

Learning-Aided Evolution for Optimization

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