PS–ABC: A hybrid algorithm based on particle swarm and artificial bee colony for high-dimensional optimization problems

Li, Zhiyong; Wei-you, Wang; Yan-yan, Yan

doi:10.1016/j.eswa.2015.07.043

Cited by 144 publications

(56 citation statements)

References 30 publications

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“…It is foreseeable that AES will become an important method of AI in computer science and related domains. In general, these methods are not only applied to solve the problem of AI independently, but also integrated with the existing methods to solve application problems [60,[70][71][72]. On the other hand, the theory and method of AI are applied to solve new problems.…”

Section: Abstract Hot Issues and Research Trend Analysismentioning

confidence: 99%

Global Research on Artificial Intelligence from 1990–2014: Spatially-Explicit Bibliometric Analysis

Niu

Tang

et al. 2016

IJGI

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Abstract:In this article, we conducted the evaluation of artificial intelligence research from 1990-2014 by using bibliometric analysis. We introduced spatial analysis and social network analysis as geographic information retrieval methods for spatially-explicit bibliometric analysis. This study is based on the analysis of data obtained from database of the Science Citation Index Expanded (SCI-Expanded) and Conference Proceedings Citation Index-Science (CPCI-S). Our results revealed scientific outputs, subject categories and main journals, author productivity and geographic distribution, international productivity and collaboration, and hot issues and research trends. The growth of article outputs in artificial intelligence research has exploded since the 1990s, along with increasing collaboration, reference, and citations. Computer science and engineering were the most frequently-used subject categories in artificial intelligence studies. The top twenty productive authors are distributed in countries with a high investment of research and development. The United States has the highest number of top research institutions in artificial intelligence, producing most single-country and collaborative articles. Although there is more and more collaboration among institutions, cooperation, especially international ones, are not highly prevalent in artificial intelligence research as expected. The keyword analysis revealed interesting research preferences, confirmed that methods, models, and application are in the central position of artificial intelligence. Further, we found interesting related keywords with high co-occurrence frequencies, which have helped identify new models and application areas in recent years. Bibliometric analysis results from our study will greatly facilitate the understanding of the progress and trends in artificial intelligence, in particular, for those researchers interested in domain-specific AI-driven problem-solving. This will be of great assistance for the applications of AI in alternative fields in general and geographic information science, in particular.

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Section: Abstract Hot Issues and Research Trend Analysismentioning

confidence: 99%

Global Research on Artificial Intelligence from 1990–2014: Spatially-Explicit Bibliometric Analysis

Niu

Tang

et al. 2016

IJGI

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“…Li, et al [137] 2015 PS-ABC High-dimensional optimization problems Sedighizadeh and Mazaheripour [138] 2017 PSO-ABC Multi objective vehicle routing problem 4.8.7. Hybridization of PSO using Other Social Metaheuristic Approaches…”

Section: Pso-de-pabc and Pso-de-gabc Optimization Problemsmentioning

confidence: 99%

Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives

Sengupta

Basak

Peters

2018

MAKE

395

184

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Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social cooperation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment and improvements of its most basic as well as some of the very recent state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.

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“…30,31 Moreover, the results are compared with those of LWPS, PSO and GA. The standard test functions are shown in Table I.…”

Section: Numerical Experimentsmentioning

confidence: 99%

A chaos wolf optimization algorithm with self-adaptive variable step-size

et al. 2017

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To explore the problem of parameter optimization for complex nonlinear function, a chaos wolf optimization algorithm (CWOA) with self-adaptive variable step-size was proposed. The algorithm was based on the swarm intelligence of wolf pack, which fully simulated the predation behavior and prey distribution way of wolves. It possessed three intelligent behaviors such as migration, summons and siege. And the competition rule as “winner-take-all” and the update mechanism as “survival of the fittest” were also the characteristics of the algorithm. Moreover, it combined the strategies of self-adaptive variable step-size search and chaos optimization. The CWOA was utilized in parameter optimization of twelve typical and complex nonlinear functions. And the obtained results were compared with many existing algorithms, including the classical genetic algorithm, the particle swarm optimization algorithm and the leader wolf pack search algorithm. The investigation results indicate that CWOA possess preferable optimization ability. There are advantages in optimization accuracy and convergence rate. Furthermore, it demonstrates high robustness and global searching ability.

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PS–ABC: A hybrid algorithm based on particle swarm and artificial bee colony for high-dimensional optimization problems

Cited by 144 publications

References 30 publications

Global Research on Artificial Intelligence from 1990–2014: Spatially-Explicit Bibliometric Analysis

Global Research on Artificial Intelligence from 1990–2014: Spatially-Explicit Bibliometric Analysis

Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives

A chaos wolf optimization algorithm with self-adaptive variable step-size

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