Evolutionary mating algorithm

Mohd Herwan Sulaiman; Zuriani Mustaffa; Mohd Mawardi Saari; Hamdan Daniyal; Seyedali Mirjalili

doi:10.1007/s00521-022-07761-w

Evolutionary mating algorithm

Mohd Herwan Sulaiman, Zuriani Mustaffa, Mohd Mawardi Saari, Hamdan Daniyal, Seyedali Mirjalili

Artificial Intelligence Research and Optimization

Research output: Contribution to journal › Article › peer-review

34 Citations (Scopus)

Abstract

This paper proposes a new evolutionary algorithm namely Evolutionary Mating Algorithm (EMA) to solve constrained optimization problems. The algorithm is based on the adoption of random mating concept from Hardy–Weinberg equilibrium and crossover index in order to produce new offspring. In this algorithm, effect of the environmental factor (i.e. the presence of predator) has also been considered and treated as an exploratory mechanism. The EMA is initially tested on the 23 benchmark functions to analyze its effectiveness in finding optimal solutions for different search spaces. It is then applied to Optimal Power Flow (OPF) problems with the incorporation of Flexible AC Transmission Systems (FACTS) devices and stochastic wind power generation. The extensive comparative studies with other algorithms demonstrate that EMA provides better results and can be used in solving real optimization problems from various fields.

Original language	English
Journal	Neural Computing and Applications
DOIs	https://doi.org/10.1007/s00521-022-07761-w
Publication status	Published - 2023

Keywords

Evolutionary mating algorithm
FACTS devices
Optimal power flow
Optimization techniques

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/s00521-022-07761-w

Cite this

@article{99a5389083f4444eb6b10cc18b40c9f5,

title = "Evolutionary mating algorithm",

abstract = "This paper proposes a new evolutionary algorithm namely Evolutionary Mating Algorithm (EMA) to solve constrained optimization problems. The algorithm is based on the adoption of random mating concept from Hardy–Weinberg equilibrium and crossover index in order to produce new offspring. In this algorithm, effect of the environmental factor (i.e. the presence of predator) has also been considered and treated as an exploratory mechanism. The EMA is initially tested on the 23 benchmark functions to analyze its effectiveness in finding optimal solutions for different search spaces. It is then applied to Optimal Power Flow (OPF) problems with the incorporation of Flexible AC Transmission Systems (FACTS) devices and stochastic wind power generation. The extensive comparative studies with other algorithms demonstrate that EMA provides better results and can be used in solving real optimization problems from various fields.",

keywords = "Evolutionary mating algorithm, FACTS devices, Optimal power flow, Optimization techniques",

author = "Sulaiman, {Mohd Herwan} and Zuriani Mustaffa and Saari, {Mohd Mawardi} and Hamdan Daniyal and Seyedali Mirjalili",

note = "Funding Information: This work was supported by the Ministry of Education Malaysia (MOE) under Fundamental Research Grant Scheme (FRGS/1/2022/ICT04/UMP/02/1) and Universiti Malaysia Pahang (#RDU220105). Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.",

year = "2023",

doi = "10.1007/s00521-022-07761-w",

language = "English",

journal = "Neural Computing and Applications",

issn = "0941-0643",

publisher = "Springer London",

}

TY - JOUR

T1 - Evolutionary mating algorithm

AU - Sulaiman, Mohd Herwan

AU - Mustaffa, Zuriani

AU - Saari, Mohd Mawardi

AU - Daniyal, Hamdan

AU - Mirjalili, Seyedali

N1 - Funding Information: This work was supported by the Ministry of Education Malaysia (MOE) under Fundamental Research Grant Scheme (FRGS/1/2022/ICT04/UMP/02/1) and Universiti Malaysia Pahang (#RDU220105). Publisher Copyright: © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

PY - 2023

Y1 - 2023

N2 - This paper proposes a new evolutionary algorithm namely Evolutionary Mating Algorithm (EMA) to solve constrained optimization problems. The algorithm is based on the adoption of random mating concept from Hardy–Weinberg equilibrium and crossover index in order to produce new offspring. In this algorithm, effect of the environmental factor (i.e. the presence of predator) has also been considered and treated as an exploratory mechanism. The EMA is initially tested on the 23 benchmark functions to analyze its effectiveness in finding optimal solutions for different search spaces. It is then applied to Optimal Power Flow (OPF) problems with the incorporation of Flexible AC Transmission Systems (FACTS) devices and stochastic wind power generation. The extensive comparative studies with other algorithms demonstrate that EMA provides better results and can be used in solving real optimization problems from various fields.

AB - This paper proposes a new evolutionary algorithm namely Evolutionary Mating Algorithm (EMA) to solve constrained optimization problems. The algorithm is based on the adoption of random mating concept from Hardy–Weinberg equilibrium and crossover index in order to produce new offspring. In this algorithm, effect of the environmental factor (i.e. the presence of predator) has also been considered and treated as an exploratory mechanism. The EMA is initially tested on the 23 benchmark functions to analyze its effectiveness in finding optimal solutions for different search spaces. It is then applied to Optimal Power Flow (OPF) problems with the incorporation of Flexible AC Transmission Systems (FACTS) devices and stochastic wind power generation. The extensive comparative studies with other algorithms demonstrate that EMA provides better results and can be used in solving real optimization problems from various fields.

KW - Evolutionary mating algorithm

KW - FACTS devices

KW - Optimal power flow

KW - Optimization techniques

UR - http://www.scopus.com/inward/record.url?scp=85138171273&partnerID=8YFLogxK

U2 - 10.1007/s00521-022-07761-w

DO - 10.1007/s00521-022-07761-w

M3 - Article

AN - SCOPUS:85138171273

SN - 0941-0643

JO - Neural Computing and Applications

JF - Neural Computing and Applications

ER -

Evolutionary mating algorithm

Abstract

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this