A study on recent bio-inspired optimization algorithms

Pazhaniraja, N.; Paul, Peter V.; Roja, G.; Shanmugapriya, K.; Sonali, B.

doi:10.1109/icscn.2017.8085674

Cited by 18 publications

(8 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of the inefficiency of classical optimization algorithms in solving large-scale combinative problems, metaheuristic optimization algorithms have been proposed to inspect the performance. This attempt goes well on the criteria of multifunctional requirements such as flexibility, gradient-free mechanism, and local optima avoidance [20]. The Salp Swarm algorithm's effectiveness in hybrid beamforming is similar to the behavior of Salp Swarms searching for food in the marine environment.…”

Section: Introductionmentioning

confidence: 88%

Hybrid Beamforming for Dual Functioning MIMO Radar Using EnhancedSocial Ski Drivers Algorithm

2023

IJIES

View full text Add to dashboard Cite

Inflated communication traffic and expanding radar applications result in frequency congestion and require extra spectrum resources. Dual-functioning multi input multi output radar benefits both sensing and communication operations via real-time cooperation. This decongests the radio frequency environment and allows a single hardware platform for both functionalities. Analog beamforming fails to provide multiplexing gain and digital beamforming is not preferred practically because of heavy power consumption. A reliable hybrid beamforming design is an inherent requirement due to the concerns related to limited RF chains, interference and the cost of fully-digital beamforming. In this paper, Enhanced-Social Ski Driver's algorithm is presented to investigate the performance of hybrid beamforming in dual-functioning MIMO radar. This study investigates location-based direction sensing for Rayleigh and Rician fading channels. This amendment provides improved performance with a minimal bit error rate, maximum gain, and significant normalized array power distribution. The simulation results show 24% reduction in bit error rate, 15% improvement in gain and persistent array power than existing social ski driver's algorithm.

show abstract

Section: Introductionmentioning

confidence: 88%

Hybrid Beamforming for Dual Functioning MIMO Radar Using EnhancedSocial Ski Drivers Algorithm

2023

IJIES

View full text Add to dashboard Cite

show abstract

“…These criteria can be applied to categorise frameworks and algorithms, but they do not allow for an unambiguous classification. Pazhaniraja et al (2017) provide an extensive set of classification criteria for metaheuristic algorithms. It contains the existence of constraints, the physical structure of the problem (optimal control or non-optimal control), the nature of the equation (linear/quadratic, polynomial or nonlinear), the values the decision variable can take (integer or real-valued), the nature of the variable (deterministic or stochastic), the separability of the function and the number of objective functions.…”

Section: Classification Schemes In Literaturementioning

confidence: 99%

Classifying Metaheuristics: Towards a unified multi-level classification system

Stegherr¹,

Heider²,

Hähner³

2020

Nat Comput

View full text Add to dashboard Cite

Metaheuristics provide the means to approximately solve complex optimisation problems when exact optimisers cannot be utilised. This led to an explosion in the number of novel metaheuristics, most of them metaphor-based, using nature as a source of inspiration. Thus, keeping track of their capabilities and innovative components is an increasingly difficult task. This can be resolved by an exhaustive classification system. Trying to classify metaheuristics is common in research, but no consensus on a classification system and the necessary criteria has been established so far. Furthermore, a proposed classification system can not be deemed complete if inherently different metaheuristics are assigned to the same class by the system. In this paper we provide the basis for a new comprehensive classification system for metaheuristics. We first summarise and discuss previous classification attempts and the utilised criteria. Then we present a multi-level architecture and suitable criteria for the task of classifying metaheuristics. A classification system of this kind can solve three main problems when applied to metaheuristics: organise the huge set of existing metaheuristics, clarify the innovation in novel metaheuristics and identify metaheuristics suitable to solve specific optimisation tasks.

show abstract

“…This research line is followed by [61], which compared the performance of different bio-inspired algorithms, again with prescribing which one to use as its primary goal. More recently, [62] examined the features of several recent bio-inspired algorithms, suggesting, on a concluding note, to which type of problem each of the examined algorithms should be applied. More specific is the work in [63], which compares several different algorithms considering its parallel implementation on GPU devices.…”

Section: Related Literature Studiesmentioning

confidence: 99%

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations

Molina,

Poyatos,

Del Ser

et al. 2020

Preprint

View full text Add to dashboard Cite

In recent years, a great variety of nature-and bio-inspired algorithms has been reported in the literature. This algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm.Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.

show abstract

A study on recent bio-inspired optimization algorithms

Cited by 18 publications

References 9 publications

Hybrid Beamforming for Dual Functioning MIMO Radar Using EnhancedSocial Ski Drivers Algorithm

Hybrid Beamforming for Dual Functioning MIMO Radar Using EnhancedSocial Ski Drivers Algorithm

Classifying Metaheuristics: Towards a unified multi-level classification system

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations

Contact Info

Product

Resources

About