Sandpiper Optimization Algorithm (SOA): A New Approach to Solve Challenging Real- Life Problems

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1. Introduction

In different real-world problems there is a need to minimize production cost, risks, and maximize reliability etc [1]. Optimization is the process of determining the best value for a decision variable of a function so as to minimize or maximize an objective function [2]. The objective of the optimization process is to determine the optimal solution that integrates distinct objectives into one [3]. Therefore a novel bio-inspired algorithm called Sandpiper Optimization Algorithm (SOA) and applies it to solve to solve challenging and high dimensionality bound constrained real problems. SOA is focuses on two natural behaviors of sandpipers which is migration and attacking.

2. Inspiration of SOA

The main inspiration of SOA is the migration and attacking behaviour of sandpipers. Sandpipers are seabirds which can be found all over the planet and they eat insects, fish, earthworms, and so on. Usually, sandpipers live in colonies [4]. They use their intelligence to find and attack the prey. The most important thing about the sandpipers is their migrating and attacking behaviors. Migration is defined as a seasonal movement of sandpipers from one place to another to locate the food rich and abundant sources that will provide required energy. Sandpipers frequently attack migrating birds over the sea when they migrate from one place to another [5]. These behaviors can be formulated in such a way that it can be associated with the objective function to be optimized.

Fig 1: Inspiration of SOA

3. Decision tree machine-learning combine with SOA to solve real-life applications

More machine-learning algorithms are hybridized with SOA to solve various software engineering problems [6]. The decision tree is a machine-learning model that employs supervised training for classification and prediction. A decision tree is a flowchart-like structure in which each internal node represents a “test” on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label and decision taken after computing all attributes [7].

Fig 2: Example of decision tree

4. Mathematical Model of SOA

The mathematical models of migration and attacking behaviors are modeled below.

4.1. Migration Behaviour

The group of sandpipers which move from one position to another during migration the new search agent position  to avoid the collision avoidance between their neighboring sandpipers is defined as;

4.2. Attacking behavior

Sandpipers generate the spiral behavior, while attacking on the prey, in the air. This behavior is described as follows.

5. Pseudo code of SOA

Fig 3: Pseudo code of SOA

6. Flowchart of SOA

Fig 4: Flowchart of SOA

7. Advantages & Disadvantages of SOA

Fig 5: Advantages & Disadvantages of SOA

8. Applications of SOA

The efficiency of SOA is investigated for solving real-existing optimization problems SOA is provided with application method to optimize the problems. The applications are as follows;

  • Constraint Handling [8].
  • Optical buffer design problem [9].
  • Pressure vessel design problem [10].
  • Speed reducer design problem [11].
  • Welded beam design problem [12].
  • Tension/ Compression spring design problem [13].
  • 25-bar truss design problem [14].
  • Rolling element bearing design problem [15].
Fig 6: Applications of SOA


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[3]. Singh P, Dhiman G (2018) Uncertainty representation using fuzzy-entropy approach: Special application in remotely sensed high-resolution satellite images (RSHRSIs). Applied Soft Computing 72:121-139. doi: 10.1016/j.asoc.2018.07.038

[4]. Singh P, Rabadiya K, Dhiman G (2018) A four-way decision-making system for the Indian summer monsoon rainfall. Modern Physics Letters B 32:1850304. doi: 10.1142/s0217984918503049

[5]. Dhiman G, Kumar V (2018) Multi-objective spotted hyena optimizer: A Multi-objective optimization algorithm for engineering problems. Knowledge-Based Systems 150:175-197. doi: 10.1016/j.knosys.2018.03.011

[6]. Singh P, Dhiman G (2018) A hybrid fuzzy time series forecasting model based on granular computing and bio-inspired optimization approaches. Journal of Computational Science 27:370-385. doi: 10.1016/j.jocs.2018.05.008

[7]. Dhiman G, Kaur A (2018) Optimizing the Design of Airfoil and Optical Buffer Problems Using Spotted Hyena Optimizer. Designs 2:28. doi: 10.3390/designs2030028

[8]. Dhiman G, Kumar V (2019) KnRVEA: A hybrid evolutionary algorithm based on knee points and reference vector adaptation strategies for many-objective optimization. Applied Intelligence 49:2434-2460. doi: 10.1007/s10489-018-1365-1

[9]. Kaur A, Kaur S, Dhiman G (2018) A quantum method for dynamic nonlinear programming technique using Schrödinger equation and Monte Carlo approach. Modern Physics Letters B 32:1850374. doi: 10.1142/s0217984918503748

[10]. Singh S (1999) Noise impact on time-series forecasting using an intelligent pattern matching technique. Pattern Recognition 32:1389-1398. doi: 10.1016/s0031-3203(98)00174-5

[11]. Han F, Zhu J (2012) Improved Particle Swarm Optimization Combined with Back propagation for Feed forward Neural Networks. International Journal of Intelligent Systems 28:271-288. doi: 10.1002/int.21569

[12]. Alba E, Dorronsoro B (2005) the Exploration/Exploitation Tradeoff in Dynamic Cellular Genetic Algorithms. IEEE Transactions on Evolutionary Computation 9:126-142. doi: 10.1109/tevc.2005.843751

[13]X. LIU and K. ZHAO, “Bacteria foraging optimization algorithm based on immune algorithm”, Journal of Computer Applications, vol. 32, no. 3, pp. 634-637, 2013. Available: 10.3724/sp.j.1087.2012.00634.

[14]D. Wolpert and W. Macready, “No free lunch theorems for optimization”, IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 67-82, 1997. Available: 10.1109/4235.585893.

[15]J. Holland, “Genetic Algorithms”, Scientific American, vol. 267, no. 1, pp. 66-72, 1992. Available: 10.1038/scientificamerican0792-66.

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