Academic Journal
A novel metaheuristic based on object-oriented programming concepts for engineering optimization
| Title: | A novel metaheuristic based on object-oriented programming concepts for engineering optimization |
|---|---|
| Authors: | Khalid M. Hosny, Asmaa M. Khalid, Wael Said, Mahmoud Elmezain, Seyedali Mirjalili |
| Source: | Alexandria Engineering Journal, Vol 98, Iss, Pp 221-248 (2024) |
| Publisher Information: | Elsevier BV, 2024. |
| Publication Year: | 2024 |
| Subject Terms: | Optimization, Artificial intelligence, Population, Metaheuristic, Metaheuristics, 02 engineering and technology, Search-based software engineering, Parallel metaheuristic, Industrial and Manufacturing Engineering, Engineering, 0203 mechanical engineering, Artificial Intelligence, Satisfiability and optimisation, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Swarm Intelligence Optimization Algorithms, Optimization problem, Inheritance, Global Optimization, Multi-Objective Optimization, Software development process, Meta-optimization, Optimization Applications, Mathematical optimization, Hybrid Optimization, Software development, Object-oriented programming, Engineering (General). Civil engineering (General), Computer science, Programming language, Algorithm, Engineering optimization, Computational Theory and Mathematics, Computer Science, Physical Sciences, Scheduling Problems in Manufacturing Systems, Exploration, TA1-2040, Convergence, Multiobjective Optimization in Evolutionary Algorithms, Mathematics, Software |
| Description: | This paper presents a novel, robust, efficient, and simple optimization algorithm called the Object-Oriented Programming Optimization Algorithm (OOPOA) for tackling constrained and unconstrained optimization problems. The algorithm is inspired by the inheritance concept of Object-Oriented programming languages, where the features of a class are classified into three types according to inheritance probability: public, private, and protected. The object-oriented programming inheritance concept is implemented in the algorithm to update the population for the next generations. The proposed algorithm ensures exploitation by selecting the solution with the highest fitness to be inherited in each iteration. It ensures exploration by applying a mutation process that helps explore wide regions in the search space. The performance of this technique is demonstrated by solving 34 different optimization tasks, including 20 standard benchmark problems, ten IEEE Congress of Evolutionary Computation benchmark test functions, and four constrained real-world engineering design problems. |
| Document Type: | Article Other literature type |
| Language: | English |
| ISSN: | 1110-0168 |
| DOI: | 10.1016/j.aej.2024.04.060 |
| DOI: | 10.25905/26761834 |
| DOI: | 10.60692/4a8cg-90f82 |
| DOI: | 10.60692/agwz7-8eq03 |
| DOI: | 10.25905/26761834.v1 |
| Access URL: | https://doaj.org/article/1652ef1e2b924ddfaab3e5a2ebe6178e |
| Rights: | CC BY CC BY NC ND |
| Accession Number: | edsair.doi.dedup.....6f60b8ec2cc992a11ce7b9600f21812f |
| Database: | OpenAIRE |
| ISSN: | 11100168 |
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| DOI: | 10.1016/j.aej.2024.04.060 |