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.
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].
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
6. Flowchart of SOA
7. 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].
Reference
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