A social learning particle swarm optimization algorithm for scalable optimization

Cheng, Ran; Jin, Yaochu

doi:10.1016/j.ins.2014.08.039

Cited by 652 publications

(288 citation statements)

References 59 publications

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“…This paper aims to push the boundary of surrogate-assisted optimization techniques by proposing a surrogate-assisted cooperative swarm optimization algorithm, SA-COSO for short, for solving highdimensional time-consuming optimization problems up to a dimension of 100. The SA-COSO consists of two cooperative PSO variants, one being a PSO with a constriction factor [52] and the other a social learning based PSO (SL-PSO) [53]. These two PSO variants cooperate in such a way that a particle in the PSO learns not only from its personal and global best particles, but also from the global best of the SL-PSO, whereas the particles in the SL-PSO may learn also from promising solutions contributed by the PSO.…”

Section: ) Local-surrogate Assisted Metaheuristic Algorithmsmentioning

confidence: 99%

“…In these variants, the comprehensive learning particle swarm optimizer (CLPSO) [59], the competitive swarm optimizer [60] and the social learning particle swarm optimization(SL-PSO) [53] showed better performance on preserving the diversity of the swarm and discouraging the premature convergence. Experimental results in [53] showed that SL-PSO has a higher computational efficiency in comparison with some representative PSO variants including CLPSO.…”

Section: A Particle Swarm Optimization Variantsmentioning

confidence: 99%

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Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems

Sun

Jin

Cheng

et al. 2017

IEEE Trans. Evol. Computat.

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339

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Abstract-Surrogate models have shown to be effective in assisting metaheuristic algorithms for solving computationally expensive complex optimization problems. The effectiveness of existing surrogate-assisted metaheuristic algorithms, however, has only been verified on low-dimensional optimization problems. In this paper, a surrogate-assisted cooperative swarm optimization algorithm is proposed, in which a surrogate-assisted particle swarm optimization algorithm and a surrogate-assisted social learning based particle swarm optimization algorithm cooperatively search for the global optimum. The cooperation between the particle swarm optimization and the social learning based particle swarm optimization consists of two aspects. First, they share promising solutions evaluated by the real fitness function. Second, the social learning based particle swarm optimization focuses on exploration while the particle swarm optimization concentrates on local search. Empirical studies on six 50-dimensional and six 100-dimensional benchmark problems demonstrate that the proposed algorithm is able to find high-quality solutions for high-dimensional problems on a limited computational budget.

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Section: ) Local-surrogate Assisted Metaheuristic Algorithmsmentioning

confidence: 99%

Section: A Particle Swarm Optimization Variantsmentioning

confidence: 99%

Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems

Sun

Jin

Cheng

et al. 2017

IEEE Trans. Evol. Computat.

Self Cite

339

131

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“…The initial points that serve as the initial PSO particles are provided by skeletonization. A social variation of PSO is introduced in [Cheng and Jin 2015], inspired by animals learning in nature from observing their peers. Each particle starts with a random solution and a fitness function is used to evaluate each solution.…”

Section: A:19mentioning

confidence: 99%

Imitation Learning

et al. 2017

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Imitation learning techniques aim to mimic human behavior in a given task. An agent (a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and actions. The idea of teaching by imitation has been around for many years; however, the field is gaining attention recently due to advances in computing and sensing as well as rising demand for intelligent applications. The paradigm of learning by imitation is gaining popularity because it facilitates teaching complex tasks with minimal expert knowledge of the tasks. Generic imitation learning methods could potentially reduce the problem of teaching a task to that of providing demonstrations, without the need for explicit programming or designing reward functions specific to the task. Modern sensors are able to collect and transmit high volumes of data rapidly, and processors with high computational power allow fast processing that maps the sensory data to actions in a timely manner. This opens the door for many potential AI applications that require real-time perception and reaction such as humanoid robots, self-driving vehicles, human computer interaction, and computer games, to name a few. However, specialized algorithms are needed to effectively and robustly learn models as learning by imitation poses its own set of challenges. In this article, we survey imitation learning methods and present design options in different steps of the learning process. We introduce a background and motivation for the field as well as highlight challenges specific to the imitation problem. Methods for designing and evaluating imitation learning tasks are categorized and reviewed. Special attention is given to learning methods in robotics and games as these domains are the most popular in the literature and provide a wide array of problems and methodologies. We extensively discuss combining imitation learning approaches using different sources and methods, as well as incorporating other motion learning methods to enhance imitation. We also discuss the potential impact on industry, present major applications, and highlight current and future research directions.

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“…Although PSO has witnessed a great success over the past two decades, its performance is still limited when the optimization problem has a highdimensional and complex search space [25,11]. In order to enhance the performance of PSO, a number of PSO variants have been proposed, including the parameter adaptation based variants [44,20], the new topological structure based variants [26,9], and the hybridization based variants [7,37], to name a few.…”

Section: The Canonical Pso Algorithmmentioning

confidence: 99%

Feature selection for high-dimensional classification using a competitive swarm optimizer

2016

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When solving many machine learning problems such as classification, there exists a large number of input features. However, not all features are relevant for solving the problem, and sometimes, including irrelevant features may deteriorate the learning performance. Therefore, it is essential to select the most relevant features, which is known as feature selection. Many feature selection algorithms have been developed, including evolutionary algorithms or particle swarm optimization (PSO) algorithms, to find a subset of the most important features for accomplishing a particular machine learning task. However, the traditional PSO does not perform well for large scale optimization problems, which degrades the effectiveness of PSO for feature selection when the number of features dramatically increases. In this paper, we propose to use a very recent PSO variant, known as competitive swarm optimizer (CSO) that was dedicated to large-scale optimization, for solving high-dimensional feature selection problems. In addition, the CSO, which was originally developed for continuous optimization, is adapted to performing feature selection that can be considered as a combinatorial optimization problem. An archive technique is also introduced to reduce computational cost. Experiments on six benchmark datasets demonstrate that compared to the canonical PSO based and a state-of-the-art PSO variant for feature selection, the proposed CSO-based

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A social learning particle swarm optimization algorithm for scalable optimization

Cited by 652 publications

References 59 publications

Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems

Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems

Imitation Learning

Feature selection for high-dimensional classification using a competitive swarm optimizer

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