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Enhanced Marine Predators Algorithm with Local Escaping Operator for Global Optimization
- Source :
- Knowledge-Based Systems. 232:107467
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- The recently introduced Marine Predators Algorithm (MPA) exhibits competitive performance in solving optimization problems. However, it often prematurely converges due to an imbalance between its exploration and exploitation capabilities. Therefore, in this paper, an improved MPA variant using a proposed Local Escaping Operator (LEO) is introduced. In the MPA, solution candidates are replaced by better candidates from the previous iteration, mimicking a memory of already visited prey-abundant areas. This indicates a weak inter-dependence between the candidates in the population and possible acceptance of new solution candidates created outside the algorithm without damaging the optimization process. Consequently, in the proposed approach, the worst candidates are replaced with solutions created by the LEO. The LEO is based on representative solutions, taking into account the relationship between predators and the characteristics of their population. The approach is experimentally compared with the state-of-the-art meta-heuristics on 82 test functions, including IEEE Congress on Evolutionary Computation test suites (CEC’14 and CEC’17) and three engineering problems. The results show the superiority of the LEO-MPA over the MPA and recent algorithms. Furthermore, in this paper, the suitability of the hybridization of meta-heuristics with the LEO is discussed and successful attempts with the best algorithms are shown.
- Subjects :
- education.field_of_study
Information Systems and Management
Optimization problem
Computer science
IEEE Congress on Evolutionary Computation
Population
Process (computing)
Management Information Systems
Operator (computer programming)
Artificial Intelligence
education
Global optimization
Algorithm
Software
Subjects
Details
- ISSN :
- 09507051
- Volume :
- 232
- Database :
- OpenAIRE
- Journal :
- Knowledge-Based Systems
- Accession number :
- edsair.doi...........5149fc77c6cbb53bfc876ed8087a9bf9