Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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This novel article presents the multi-objective version of the recently proposed the Whale Optimization Algorithm (WOA) known as Non-Dominated Sorting Whale Optimization Algorithm (NSWOA). This proposed NSWOA algorithm works in such a manner that it first collects all non-dominated Pareto optimal solutionsin achieve till the evolution of last iteration limit. The best solutions are then chosen from the collection of all Pareto optimal solutions using a crowding distance mechanism based on the coverage of solutions and bubblenet hunting strategy to guide humpback whales towards the dominated regions of multi-objective search spaces.For validate the efficiency and effectiveness of proposed NSWOA algorithm is applied to a set of standard unconstrained, constrained and engineering design problems. The results are verified by comparing NSWOA algorithm against Multi objective Colliding Bodies Optimizer (MOCBO), Multi objective Particle Swarm Optimizer (MOPSO), non-dominated sorting genetic algorithm II (NSGA-II) and Multi objective Symbiotic Organism Search (MOSOS).The results of proposed NSWOAalgorithm validates its efficiency in terms of Execution Time (ET) and effectiveness in terms of Generalized Distance (GD), Diversity Metric (DM) on standard unconstraint, constraint and engineering design problem in terms of high coverage andfaster convergence.
Pradeep Jangir. 2017. \u201cNon-Dominated Sorting Whale Optimization Algorithm (NSWOA): A Multi-Objective Optimization algorithm for Solving Engineering Design Problems\u201d. Global Journal of Research in Engineering - F: Electrical & Electronic GJRE-F Volume 17 (GJRE Volume 17 Issue F4): .
Crossref Journal DOI 10.17406/gjre
Print ISSN 0975-5861
e-ISSN 2249-4596
The methods for personal identification and authentication are no exception.
The methods for personal identification and authentication are no exception.
Total Score: 107
Country: India
Subject: Global Journal of Research in Engineering - F: Electrical & Electronic
Authors: Dr. Pradeep Jangir, Narottam Jangir (PhD/Dr. count: 1)
View Count (all-time): 231
Total Views (Real + Logic): 3382
Total Downloads (simulated): 1744
Publish Date: 2017 09, Mon
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This novel article presents the multi-objective version of the recently proposed the Whale Optimization Algorithm (WOA) known as Non-Dominated Sorting Whale Optimization Algorithm (NSWOA). This proposed NSWOA algorithm works in such a manner that it first collects all non-dominated Pareto optimal solutionsin achieve till the evolution of last iteration limit. The best solutions are then chosen from the collection of all Pareto optimal solutions using a crowding distance mechanism based on the coverage of solutions and bubblenet hunting strategy to guide humpback whales towards the dominated regions of multi-objective search spaces.For validate the efficiency and effectiveness of proposed NSWOA algorithm is applied to a set of standard unconstrained, constrained and engineering design problems. The results are verified by comparing NSWOA algorithm against Multi objective Colliding Bodies Optimizer (MOCBO), Multi objective Particle Swarm Optimizer (MOPSO), non-dominated sorting genetic algorithm II (NSGA-II) and Multi objective Symbiotic Organism Search (MOSOS).The results of proposed NSWOAalgorithm validates its efficiency in terms of Execution Time (ET) and effectiveness in terms of Generalized Distance (GD), Diversity Metric (DM) on standard unconstraint, constraint and engineering design problem in terms of high coverage andfaster convergence.
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