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This paper proposes a novel multi-objective optimization approach for solving multimodal optimization problems (MMOPs). An MMOP at hand is first transformed into a bi-objective optimization problem. The two objectives are constructed totally conflict by using the distance information and the objective function value. In this way, multiple optima of an MMOP are converted into the non-dominated solutions...
Cooperative coevolution (CC) provides a powerful divide-and-conquer architecture for large scale global optimization (LSGO). However, its performance relies highly on decomposition. To make near-optimal decomposition, most developed decomposition strategies either cannot obtain the correct interdependency information or require a lot of fitness evaluations (FEs) in the identification. To alleviate...
Cooperative coevolution framework is an effective strategy to deal with large scale optimization problems. However, most studies on cooperative coevolution framework utilize the same optimizer for all subcomponents, which may not be effective enough. In this paper, we propose a novel multi-optimizer cooperative coevolution method for large scale optimization problems which randomly chooses an optimization...
The aim of multimodal optimization is to locate multiple optima of a given problem. Evolutionary algorithms (EAs) are one of the most promising candidates for multimodal optimization. However, due to the use of greedy selection operators, the population of an EA will generally converge to one region of attraction. By incorporating a well-designed selection operator that can facilitate the formation...
Human detection is a significant and challenging task with applications in various domains. In real-time systems, the speed of detection is crucial to the performance of system, while the accuracy is also taken into consideration. In this work, a human detection approach based on Histograms of Oriented Gradients (HOG) feature and differential evolution (DE), termed as HOG-SVM-DE, is proposed to achieve...
Multimodal optimization aims at locating multiple optima in a run, which has two main advantages over traditional single objective global optimization. First, it would be useful to provide multiple solutions since some solutions may be hard to realize physically. Second, a multimodal algorithm is not so easy to get stuck in a local optimum. In recent years, multi-population evolutionary algorithms...
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