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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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Stock exchange trading optimization algorithm: a human-inspired method for global optimization.

¹University of Bonab, Bonab, Iran.

The Journal of Supercomputing

|June 30, 2021

Summary

This summary is machine-generated.

A new optimization algorithm, stock exchange trading optimization (SETO), mimics trader behavior to solve complex numerical and engineering problems effectively. SETO demonstrates competitive performance, achieving global optima on most tested functions and engineering challenges.

Keywords:

Engineering design problems Human-inspired meta-heuristic Numerical optimization Stock exchange trading optimization (SETO) algorithm

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Area of Science:

Computational Intelligence
Optimization Algorithms
Metaheuristics

Background:

Optimization problems are prevalent in numerical and engineering fields.
Existing meta-heuristic algorithms face challenges in complex, high-dimensional problems.
Inspiration from real-world systems can lead to novel optimization strategies.

Purpose of the Study:

Introduce a novel human-inspired optimization algorithm: Stock Exchange Trading Optimization (SETO).
Evaluate SETO's performance on numerical and engineering optimization tasks.
Compare SETO against established meta-heuristic optimizers.

Main Methods:

Developed SETO based on stock market trading strategies (technical analysis).
Incorporated three operators: rising, falling, and exchange, to guide search agents.
Tested SETO on 40 single-objective unconstrained numerical functions and 4 engineering design problems.
Compared performance against seven popular meta-heuristic algorithms.

Main Results:

SETO achieved the global optimum on 36 out of 40 numerical functions.
SETO obtained the best results on 3 out of 4 engineering problems.
Demonstrated competitive and promising performance, especially on 1000-dimension problems.

Conclusions:

SETO is a capable and effective optimization algorithm inspired by financial markets.
The algorithm shows strong performance across various problem types and dimensions.
SETO offers a promising alternative for solving complex optimization challenges.