multi-modal multi-objective optimization refers to multi-objective optimization problems that have more than one Pareto set. This paper proposes a new algorithm, modifying both the tournament and the environment selec...
详细信息
ISBN:
(纸本)9781728190488
multi-modal multi-objective optimization refers to multi-objective optimization problems that have more than one Pareto set. This paper proposes a new algorithm, modifying both the tournament and the environment selections. The purpose of this paper is to utilize the full potential of the parental population to produce diverse offspring and avoid producing duplicated solutions. In order to achieve the goal, we modify the selection mechanisms, which usually enforce the selection of solutions with high quality. This can lead to duplicates in the mating pool. Using the modified tournament selection, we aim to enable the algorithm to select diverse solutions in sparse regions of the search space. Our experiments agree with our expectations, confirming that our proposed algorithm is more effective than current competitor algorithms at approximating Pareto sets in most multimodalmulti-objective optimization problems with different number of decision variables and objective functions.
Improving the accuracy of wind speed forecasting is essential for the usage of wind energy. This paper proposes an evolutionary ensemble learning (EEL) method, which consists of ensemble empirical mode decomposition (...
详细信息
ISBN:
(纸本)9781728183923
Improving the accuracy of wind speed forecasting is essential for the usage of wind energy. This paper proposes an evolutionary ensemble learning (EEL) method, which consists of ensemble empirical mode decomposition (EEMD), random vector functional link network (RVFL) based ensemble learning, and grid-based multimodal multi-objective evolutionary algorithm (MMOG). Based on MMOG, the proposed ensemble learning model is improved in terms of accuracy. Several benchmark forecast methods are compared with the proposed EEL model on 12 wind speed forecasting datasets. The experiment results validate the superiority of the proposed EEL model in wind speed forecasting.
In recent years, numerous efficient and effective multimodal multi-objective evolutionary algorithms (MMOEAs) have been developed to address multimodalmulti-objective optimization problems (MMOPs) involving multiple ...
详细信息
ISBN:
(纸本)9781665441957
In recent years, numerous efficient and effective multimodal multi-objective evolutionary algorithms (MMOEAs) have been developed to address multimodalmulti-objective optimization problems (MMOPs) involving multiple equivalent sets of Pareto optimal solutions to be found simultaneously. However, the Pareto optimal solutions may have various contracting or expending shapes, and have random locations in the decision space. In addition, uniform decision distribution does not imply good objective distribution. Therefore, many existing MMOEAs are very difficult to guide the individuals converged to every Pareto subregion with good distribution in both the decision space and the objective space. In this paper, we present a multi-population evolutionaryalgorithm to search for the equivalent global Pareto optimal solutions. The original population should be divided into two groups of subpopulations with equal size. The first subpopulation is designed to search for the optimal solutions in objective space. At the same time. the second subpopulation focus to obtain high-quality optimal solutions in the decision space. The multi-population strategy is helpful to improve the decision and objective distributions simultaneously, and address the MMOPs effectively. The proposed algorithm is compared against five state-of-the-art MMOEAs. The experimental results indicate the proposed algorithm provides better performance than competing MMOEAs on IEEE CEC 2019 MMOPs test suite.
In recent years, multi-objective optimization has attracted a lot of attention in the field of high performance computing. In this paper, two-stage dual-archive fireworks algorithm (TSDA_MMOFWA) is proposed to solve t...
详细信息
ISBN:
(纸本)9781450387477
In recent years, multi-objective optimization has attracted a lot of attention in the field of high performance computing. In this paper, two-stage dual-archive fireworks algorithm (TSDA_MMOFWA) is proposed to solve the multimodalmulti-objective optimization problems (MMOPs). The first stage of the algorithm uses a dual-archive genetic operator evolution strategy. Each firework performs a relatively independent decision space search guided by global information to find a large number of Pareto optimal solution set. The second stage of the algorithm distributes the solution set uniformly over multiple approximate Pareto optimal sets by means of an adaptive firework explosion strategy. The performance of the algorithm is evaluated on 16 benchmark MMOPs. The experimental results show that the proposed TSDA_MMOFWA possesses a competitive performance compared to the state-of-the-art comparison algorithms.
暂无评论