A novel nature-inspired algorithm for optimization: Squirrel search algorithm

Jain, Mamta; Singh, Vijander; Rani, Asha

doi:10.1016/j.swevo.2018.02.013

Cited by 766 publications

(440 citation statements)

References 77 publications

Supporting

Mentioning

436

Contrasting

Unclassified

Order By: Relevance

“…Another important algorithm of this category is ACO, designed by mimicking the foraging process of the ant species. Several popular and some recent algorithms of this category are: Shuffled frog leaping algorithm [25], Bacterial foraging [26], Artificial bee colony [27], Firefly algorithm [28], Grey Wolf optimizer (GWO) [29], Ant Lion optimizer (ALO) [30], Whale optimization algorithm [31], Grasshopper optimization algorithm (GOA) [32], Squirrel search algorithm [33], Harris Hawks optimization (HHO) [34] etc.…”

Section: Related Workmentioning

confidence: 99%

Groundwater Flow Algorithm: A Novel Hydro-geology based Optimization Algorithm

Guha¹,

Ghosh²,

Ghosh³

et al. 2020

Preprint

View full text Add to dashboard Cite

A novel meta-heuristic nature-inspired optimization algorithm known as Groundwater Flow Algorithm (GWFA) is proposed in this paper. GWFA is inspired from the movement of groundwater from recharge areas to discharge areas. It follows a position update procedure guided by Darcy's law which provides a mathematical framework of groundwater flow. The proposed optimization algorithm has been evaluated on 23 benchmark functions. The significance of the results is statistically validated using Wilcoxon rank-sum, Friedman and Kruskal Walis tests. To prove the robustness of the algorithm, it has been further applied on several standard engineering problems. From these exhaustive experiments, it has been observed that the proposed GWFA can outperform many state-of-the-art optimization algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

Groundwater Flow Algorithm: A Novel Hydro-geology based Optimization Algorithm

Guha¹,

Ghosh²,

Ghosh³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In recent years, inspired by natural phenomena, a variety of novel meta-heuristic algorithms have been reported, e.g., bat algorithm (BA) [23], amoeboid organism algorithm [24], animal migration optimization (AMO) [25], artificial plant optimization algorithm (APOA) [26], biogeography-based optimization (BBO) [27,28], human learning optimization (HLO) [29], krill herd (KH) [30][31][32], monarch butterfly optimization (MBO) [33], elephant herding optimization (EHO) [34], invasive weed optimization (IWO) algorithm [35], earthworm optimization algorithm (EWA) [36], squirrel search algorithm (SSA) [37], butterfly optimization algorithm (BOA) [38], salp swarm algorithm (SSA) [39], whale optimization algorithm (WOA) [40], and others. A review of swarm intelligence algorithms can be referred to [41].…”

Section: Of 31mentioning

confidence: 99%

A Novel Monarch Butterfly Optimization with Global Position Updating Operator for Large-Scale 0-1 Knapsack Problems

Feng

Wang

2019

Mathematics

View full text Add to dashboard Cite

As a significant subset of the family of discrete optimization problems, the 0-1 knapsack problem (0-1 KP) has received considerable attention among the relevant researchers. The monarch butterfly optimization (MBO) is a recent metaheuristic algorithm inspired by the migration behavior of monarch butterflies. The original MBO is proposed to solve continuous optimization problems. This paper presents a novel monarch butterfly optimization with a global position updating operator (GMBO), which can address 0-1 KP known as an NP-complete problem. The global position updating operator is incorporated to help all the monarch butterflies rapidly move towards the global best position. Moreover, a dichotomy encoding scheme is adopted to represent monarch butterflies for solving 0-1 KP. In addition, a specific two-stage repair operator is used to repair the infeasible solutions and further optimize the feasible solutions. Finally, Orthogonal Design (OD) is employed in order to find the most suitable parameters. Two sets of low-dimensional 0-1 KP instances and three kinds of 15 high-dimensional 0-1 KP instances are used to verify the ability of the proposed GMBO. An extensive comparative study of GMBO with five classical and two state-of-the-art algorithms is carried out. The experimental results clearly indicate that GMBO can achieve better solutions on almost all the 0-1 KP instances and significantly outperforms the rest.

show abstract

“…The FOA's process is similar to as with the other swarm optimization algorithms. The first phase of the fruit flies' quest for food is initiated with a random uniform distribution [6,[28][29][30][31][32], with no specific position or direction. In the second phase, the fruit fly with the best sense of smell or the best fitness from the first phase within the group is determined.…”

Section: The Fruit Fly Optimization Algorithmmentioning

confidence: 99%

“…Hence the continual development of a new algorithm is required. As can be seen the details and the broadly classified the nature-inspired algorithms, which presented the development of algorithms into the current modern literatures [6,7].…”

Section: Introductionmentioning

confidence: 99%

A Cooperation of Multileader Fruit Fly and the Probabilistic Random Walk with Adaptive Normalization for Solving Solution of the Unconstrained Optimization Problems

Apinantanakon¹,

Sunat²,

Chiewchanwattana³

2018

Preprint

View full text Add to dashboard Cite

A swarm based nature-inspired optimization algorithm namely fruit fly optimization algorithm (FOA) has simple structure and ease of implementation. However, FOA has a low success rate and a slow convergence because FOA generates new positions around the best location using fixed search radius. Several improved FOAs have been proposed. But their exploration ability is questionable. To make the search process to transit from the exploration phase to the exploitation phase smoothly, this paper proposes a new FOA constructed from a cooperation of the multileader and the probabilistic random walk strategies (CPFOA). It has two population types working together. CPFOA's performance is evaluated by 18 well-known standard benchmark, and 30 CEC’2017 functions. The results showed that CPFOA outperforms both the original FOA and its variants in terms of convergence speed and performance accuracy. The results base on CEC’2017 show that CPFOA can achieve a very promising accuracy when compared with the well-known competitive algorithms. CPFOA is applied to optimize two applications; the MLPs classifying real datasets and extracting parameters of T-S fuzzy system for modelling Box and Jenkins gas furnace data set. CPFOA can find parameters having a very high quality compared with the best known competitive algorithms.

show abstract

A novel nature-inspired algorithm for optimization: Squirrel search algorithm

Cited by 766 publications

References 77 publications

Groundwater Flow Algorithm: A Novel Hydro-geology based Optimization Algorithm

Groundwater Flow Algorithm: A Novel Hydro-geology based Optimization Algorithm

A Novel Monarch Butterfly Optimization with Global Position Updating Operator for Large-Scale 0-1 Knapsack Problems

A Cooperation of Multileader Fruit Fly and the Probabilistic Random Walk with Adaptive Normalization for Solving Solution of the Unconstrained Optimization Problems

Contact Info

Product

Resources

About