Using Double Well Function as a Benchmark Function for Optimization Algorithm

This paper proposes a novel search method for a swarm of quadcopter drones. In the proposed method, inspired by the phenomena of swarms in nature, drones effectively look for the search target by investigating the evidence from the surroundings and communicating with each other. The position update mechanism is implemented using the particle swarm optimization algorithm as the swarm intelligence (a well-known swarm-based optimization algorithm), as well as a dynamic model for the drones to take the real-world environment into account. In addition, the mechanism is processed in real-time along with the movements of the drones. The effectiveness of the proposed method was verified through repeated test simulations, including a benchmark function optimization and air pollutant search problems. The results show that the proposed method is highly practical, accurate, and robust.

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A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture

Communications in Advanced Mathematical Sciences ◽

10.33434/cams.459423 ◽

2018 ◽

Vol 1 (1) ◽

pp. 67-83

Author(s):

Javier Luis Mroginski ◽

Hugo Guillermo Castro

Keyword(s):

Optimization Algorithm ◽

Benchmark Function ◽

Metaheuristic Optimization ◽

Metaheuristic Optimization Algorithm ◽

Gpu Architecture

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Comparative Study of Meta-heuristics Optimization Algorithm using Benchmark Function

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v7i3.pp1643-1650 ◽

2017 ◽

Vol 7 (3) ◽

pp. 1643 ◽

Cited By ~ 2

Author(s):

I. Ismail ◽

A. Hanif Halim

Keyword(s):

Optimization Algorithm ◽

Real World ◽

Computation Time ◽

Global Optimum ◽

Performance Comparison ◽

Difficulty Level ◽

Problem Type ◽

Benchmark Function ◽

Real World Application ◽

Real World Problems

Meta-heuristics optimization is becoming a popular tool for solving numerous problems in real-world application due to the ability to overcome many shortcomings in traditional optimization. Despite of the good performance, there is limitation in some algorithms that deteriorates by certain degree of problem type. Therefore it is necessary to compare the performance of these algorithms with certain problem type. This paper compares 7 meta-heuristics optimization with 11 benchmark functions that exhibits certain difficulties and can be assumed as a simulation relevant to the real-world problems. The tested benchmark function has different type of problem such as modality, separability, discontinuity and surface effects with steep-drop global optimum, bowl- and plateau-typed function. Some of the proposed function has the combination of these problems, which might increase the difficulty level of search towards global optimum. The performance comparison includes computation time and convergence of global optimum.

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