TY - JOUR
T1 - A novel Whale Optimization Algorithm integrated with Nelder–Mead simplex for multi-objective optimization problems
AU - Abdel-Basset, Mohamed
AU - Mohamed, Reda
AU - Mirjalili, Seyedali
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2021/1/5
Y1 - 2021/1/5
N2 - Recently, several meta-heuristics and evolutionary algorithms have been proposed for tackling optimization problems. Such methods tend to suffer from degraded performance when solving multi-objective optimization problems due to addressing the conflicting goals of finding accurate estimation of Pareto optimal solutions and increasing their distribution across all objectives. In this paper, the Whale Optimization Algorithm (WOA) is improved and extended to solve such multi-objective optimization problems with the purpose of alleviating these drawbacks. The improvements include: (1) modifying the distance control factor of the standard WOA to contain values generated dynamically instead of a fixed one, (2) the trade-off between moving toward the opposite of the best solution and its original values based on a certain probability to prevent stuck into local minima, and (3) accelerating the convergence and coverage using Nelder–Mead method and the Pareto Archived Evolution Strategy (PAES). The proposed algorithm is tested on three benchmark multi-objective test functions (DTLZ, CEC 2009, and GLT), including 25 test functions, to verify its effectiveness by comparing with nine robust multi-objective algorithms. The experiments demonstrate the superiority of the proposed algorithm compared to some of the existing multi-objective algorithms in the literature.
AB - Recently, several meta-heuristics and evolutionary algorithms have been proposed for tackling optimization problems. Such methods tend to suffer from degraded performance when solving multi-objective optimization problems due to addressing the conflicting goals of finding accurate estimation of Pareto optimal solutions and increasing their distribution across all objectives. In this paper, the Whale Optimization Algorithm (WOA) is improved and extended to solve such multi-objective optimization problems with the purpose of alleviating these drawbacks. The improvements include: (1) modifying the distance control factor of the standard WOA to contain values generated dynamically instead of a fixed one, (2) the trade-off between moving toward the opposite of the best solution and its original values based on a certain probability to prevent stuck into local minima, and (3) accelerating the convergence and coverage using Nelder–Mead method and the Pareto Archived Evolution Strategy (PAES). The proposed algorithm is tested on three benchmark multi-objective test functions (DTLZ, CEC 2009, and GLT), including 25 test functions, to verify its effectiveness by comparing with nine robust multi-objective algorithms. The experiments demonstrate the superiority of the proposed algorithm compared to some of the existing multi-objective algorithms in the literature.
KW - Algorithm
KW - Artificial Intelligence
KW - Dynamic distance
KW - Multi-objective optimization
KW - Nelder–Mead method
KW - Opposition based learning
KW - Optimization
KW - Swarm Intelligence
KW - Whale Optimization Algorithm
KW - WOA
UR - http://www.scopus.com/inward/record.url?scp=85097352655&partnerID=8YFLogxK
U2 - 10.1016/j.knosys.2020.106619
DO - 10.1016/j.knosys.2020.106619
M3 - Article
AN - SCOPUS:85097352655
SN - 0950-7051
VL - 212
SP - 1
EP - 28
JO - Knowledge-Based Systems
JF - Knowledge-Based Systems
M1 - 106619
ER -