A Comprehensive Review of Swarm Optimization Algorithms

Wahab, Mohd Nadhir Ab; Nefti‐Meziani, Samia; Atyabi, Adham

doi:10.1371/journal.pone.0122827

Cited by 429 publications

(212 citation statements)

References 111 publications

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“…The Artificial Bee Colony (ABC) method, presented by Dervis Karaboga in 2005 [19], was inspired by the search behaviour of bee colonies. Honeybees use several mechanisms, such as a waggle dance, to locate food sources.…”

Section: Artificial Bee Colony Methodsmentioning

confidence: 99%

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

2017

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Advanced global optimization algorithms have been continuously introduced and improved to solve various complex design optimization problems for which the objective and constraint functions can only be evaluated through computation intensive numerical analyses or simulations with a large number of design variables. The often implicit, multimodal, and ill-shaped objective and constraint functions in high-dimensional and "black-box" forms demand the search to be carried out using low number of function evaluations with high search efficiency and good robustness. This work investigates the performance of six recently introduced, nature-inspired global optimization methods: Artificial Bee Colony (ABC), Firefly Algorithm (FFA), Cuckoo Search (CS), Bat Algorithm (BA), Flower Pollination Algorithm (FPA) and Grey Wolf Optimizer (GWO). These approaches are compared in terms of search efficiency and robustness in solving a set of representative benchmark problems in smooth-unimodal, non-smooth unimodal, smooth multimodal, and non-smooth multimodal function forms. In addition, four classic engineering optimization examples and a real-life complex mechanical system design optimization problem, floating offshore wind turbines design optimization, are used as additional test cases representing computationally-expensive black-box global optimization problems. Results from this comparative study show that the ability of these global optimization methods to obtain a good solution diminishes as the dimension of the problem, or number of design variables increases. Although none of these methods is universally capable, the study finds that GWO and ABC are more efficient on average than the other four in obtaining high quality solutions efficiently and consistently, solving 86% and 80% of the tested benchmark problems, respectively. The research contributes to future improvements of global optimization methods.

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Section: Artificial Bee Colony Methodsmentioning

confidence: 99%

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

2017

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“…A detailed discussion on variations of evolutionary methods and their performances can be found in (Ab Wahab et al, 2015). …”

Section: Methodsmentioning

confidence: 99%

Mixture of autoregressive modeling orders and its implication on single trial EEG classification

Atyabi

Shic

Naples

2016

Expert Systems with Applications

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Autoregressive (AR) models are of commonly utilized feature types in Electroencephalogram (EEG) studies due to offering better resolution, smoother spectra and being applicable to short segments of data. Identifying correct AR’s modeling order is an open challenge. Lower model orders poorly represent the signal while higher orders increase noise. Conventional methods for estimating modeling order includes Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Final Prediction Error (FPE). This article assesses the hypothesis that appropriate mixture of multiple AR orders is likely to better represent the true signal compared to any single order. Better spectral representation of underlying EEG patterns can increase utility of AR features in Brain Computer Interface (BCI) systems by increasing timely & correctly responsiveness of such systems to operator’s thoughts. Two mechanisms of Evolutionary-based fusion and Ensemble-based mixture are utilized for identifying such appropriate mixture of modeling orders. The classification performance of the resultant AR-mixtures are assessed against several conventional methods utilized by the community including 1) A well-known set of commonly used orders suggested by the literature, 2) conventional order estimation approaches (e.g., AIC, BIC and FPE), 3) blind mixture of AR features originated from a range of well-known orders. Five datasets from BCI competition III that contain 2, 3 and 4 motor imagery tasks are considered for the assessment. The results indicate superiority of Ensemble-based modeling order mixture and evolutionary-based order fusion methods within all datasets.

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“…The most widely used metaheuristic algorithms in scientific applications are: Genetic Algorithm (GA) [44], Particle Swarm Optimization algorithm (PSO) [45], Differential Evolution (DE) [46], Artificial Bee Colony (ABC) algorithm [47] and Cuckoo Search Algorithm (CSA) [48]. More recently, Wahab et al [49] provided a comprehensive evaluation of the performance of various meta heuristic algorithms in solving a set of thirty benchmark functions. In their experiments, the benchmark selected for evaluation differs in their characteristics and it includes unimodal, multimodal, separable and inseparable functions.…”

Section: Pso-based Rank List Fusion Algorithmmentioning

confidence: 99%

On integrating re-ranking and rank list fusion techniques for image retrieval

Arun

Govindan

Kumar

2017

Int J Data Sci Anal

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This paper aims to unify image re-ranking and rank aggregation strategies to enhance the retrieval precision of content-based image retrieval (CBIR) systems. In general, CBIR systems are concerned with the retrieval of a set of relevant images from large repositories in response to a submitted query. The primary objective of CBIR systems is the exact ordering of database images in accordance with the presented query. To this end, we present a novel image re-ranking scheme for reordering the initial search results returned by multiple retrieval models and an efficient rank list fusion scheme to combine these refined retrieval results to achieve better performance. The re-ranking algorithm introduced in this work utilizes distance correlation coefficient to refine the search result generated by a given retrieval model. It involves two-step clustering of the initial retrieval list followed by an adaptive procedure for updating the similarity scores among images based on the created clusters. Similarly, the Particle Swarm Optimization-based similarity score fusion framework presented in this work optimally combines the retrieval results generated by multiple CBIR systems. The proposed approach is evaluated on various retrieval tasks using state-of-the-art low-level and high-level descriptors. Experimental results show that our model can significantly enhance the overall effectiveness of CBIR systems.

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A Comprehensive Review of Swarm Optimization Algorithms

Cited by 429 publications

References 111 publications

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

Mixture of autoregressive modeling orders and its implication on single trial EEG classification

On integrating re-ranking and rank list fusion techniques for image retrieval

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