Multi-objective multi-criteria evolutionary algorithm for multi-objective multi-task optimization

Du, Ke-Lin; Li, Jianyu; Wang, Hua; Zhang, Jun

doi:10.1007/s40747-022-00650-8

Cited by 14 publications

(8 citation statements)

References 60 publications

(101 reference statements)

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“…There were positive correlations between these multiobjective optimization problems. Therefore, we plan to employ evolutionary multiobjective multitask optimization algorithms in the future to enhance both the solution accuracy and efficiency [27][28][29]. In addition, because of the high computational requirements of the system, we intend to consider data-driven evolutionary algorithms [30,31] to deal with it.…”

Section: Discussionmentioning

confidence: 99%

Reentry Capsule Reachable Tube Boundary Prediction via Evolutionary Multiobjective Optimization

Zou,

Cui,

et al. 2024

International Journal of Aerospace Engineering

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In the field of aerospace, solving the boundary problem associated with the parachute-capsule system remains a big challenge. The conventional Monte Carlo method proves inadequate for acquiring comprehensive boundary information. To address this issue, this paper introduces a novel tube prediction scheme by leveraging the natural geometric characteristics of the reachable tube and employing a multiobjective optimization strategy. Initially, a multibody dynamic model with nine degrees of freedom was established and verified by the airdrop test data to ensure the accuracy and reliability of the model. Subsequently, the Sobol sensitivity analysis method was employed to assess uncertain factors that affect the deceleration phase of the reentry capsule. These factors are then utilized to determine the optimization parameters for the multiobjective optimization model. Ultimately, the multiobjective evolutionary algorithm based on decomposition was employed to solve the multiobjective optimization model, and the geometric boundary of the tube corresponds to the Pareto front of the multiobjective optimization. The proposed methodology was validated through a simulation experiment utilizing the Chang’e-5 reentry capsule as an engineering case. The experimental results unequivocally demonstrate the superior accuracy of our approach in predicting the boundary of the reachable tube compared to the Monte Carlo method. This research serves as a valuable reference for calculating reachable tubes in practical engineering scenarios and can be effectively applied to spacecraft search and rescue operations during the reentry phase.

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Section: Discussionmentioning

confidence: 99%

Reentry Capsule Reachable Tube Boundary Prediction via Evolutionary Multiobjective Optimization

Zou,

Cui,

et al. 2024

International Journal of Aerospace Engineering

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“…Evolutionary Computation EC algorithms can be divided into two categories, including evolutionary algorithm (EA) and swarm intelligence (SI) [2]. Herein, the following contents introduce two typical and widely-used EC algorithms for optimization problems, i.e., the PSO [24] as a typical SI and the DE [23] as a typical EA.…”

Section: Background and Related Workmentioning

confidence: 99%

“…EC algorithms are promising because they do not require the strict mathematical characteristics of the problem and can find the global optimum or near-global optimum within an acceptable time. Generally, EC is a common framework that simulates the evolutionary mechanism of biology (e.g., GA [14] and differential evolution (DE) [20]- [23]) and the swarm intelligence behaviors of animals/insects (e.g., particle swarm optimization (PSO) [24]- [26] and ant colony optimization (ACO) [27]- [30])). The core idea of EC algorithms is "survival of the fittest".…”

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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“…In order to deal with the relationship among several conflicting objectives, substantial multi-objective evolutionary algorithms (MOEAs) have been proposed over the past few decades and shown excellent performance in solving unconstrained multi-objective optimization problems. These MOEAs can be classified into three categories based on their selection mechanisms: the method of dominance-based [6], the method of decomposition-based [7], and the method of indicator-based [8], in which the first method have received a lot of attention due to their simplicity and ease of implementation. However, none of these methods can be directly used to solve CMOPs since they cannot effectively handle constraints.…”

Section: Introductionmentioning

confidence: 99%

A dual-population constrained multi-objective evolutionary algorithm with variable auxiliary population size

Liang

Chen

Wang

et al. 2023

Complex Intell. Syst.

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Constrained multi-objective optimization problems (CMOPs) exist widely in the real world, which simultaneously contain multiple constraints to be satisfied and multiple conflicting objectives to be optimized. Therefore, the challage in addressing CMOPs is how to better balance constraints and objectives. To remedy this issue, this paper proposes a novel dual-population based constrained multi-objective evolutionary algorithm to solve CMOPs, in which two populations with different functions are employed. Specifically, the main population considers both objectives and constraints for solving the original CMOPs, while the auxiliary population is used only for optimization of objectives without considering constraints. In addition, a dynamic population size reducing mechanism is proposed, which is used to adjust the size of the auxiliary population, so as to reduce the consumption of computing resoruces in the later stage. Moreover, an independent external archive is set to store feasible solutions found by the auxiliary population, so as to provide high-quality feasible solutions for the main population. The experimental results on 55 benchmark functions show that the proposed algorithm exhibits superior or at least competitive performance compared to other state-of-the-art algorithms.

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Multi-objective multi-criteria evolutionary algorithm for multi-objective multi-task optimization

Cited by 14 publications

References 60 publications

Reentry Capsule Reachable Tube Boundary Prediction via Evolutionary Multiobjective Optimization

Reentry Capsule Reachable Tube Boundary Prediction via Evolutionary Multiobjective Optimization

Learning-Aided Evolution for Optimization

A dual-population constrained multi-objective evolutionary algorithm with variable auxiliary population size

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