Abstract
Nature-inspired computing has attracted huge attention since its origin, especially in the field of multiobjective optimization. This paper proposes a disruption-based multiobjective equilibrium optimization algorithm (DMOEOA). A novel mutation operator named layered disruption method is integrated into the proposed algorithm with the aim of enhancing the exploration and exploitation abilities of DMOEOA. To demonstrate the advantages of the proposed algorithm, various benchmarks have been selected with five different multiobjective optimization algorithms. The test results indicate that DMOEOA does exhibit better performances in these problems with a better balance between convergence and distribution. In addition, the new proposed algorithm is applied to the structural optimization of an elastic truss with the other five existing multiobjective optimization algorithms. The obtained results demonstrate that DMOEOA is not only an algorithm with good performance for benchmark problems but is also expected to have a wide application in real-world engineering optimization problems.
Highlights
Conventional mathematical optimization methods have the disadvantage of getting trapped in local optima for nonlinear optimization problems
On the basis of absorbing the excellent searching mechanism of the equilibrium optimization algorithm and a novel mutation operator proposed in this work, this paper presented a disruption-based multiobjective equilibrium optimization algorithm (DMOEOA), which is able to handle multiobjective optimization problems. e novel mutation operator named layered disruption method is first proposed in this work with the aim of enhancing the exploration and exploitation abilities of DMOEOA
Five multiobjective optimization algorithms, including multi objective particle swarm optimization (MOPSO), multiobjective ant lion optimizer (MOALO), multiobjective whale optimization (MOWOA), NSGAII, and multiobjective grey wolf algorithm (MOGWO), are employed to compare with DMOEOA. e parameters of algorithms shown in Table 2 are chosen. ese parameters are selected in accordance with the original algorithms
Summary
Conventional mathematical optimization methods have the disadvantage of getting trapped in local optima for nonlinear optimization problems Such optimization algorithms are highly complex and specialized. En, a series of initial multiobjective optimization algorithms based on Pareto optimal were proposed successively, such as multiple objective genetic algorithms (MOGA) [16], niched Pareto genetic algorithm (NPGA) [17], and a nondominated sorting genetic algorithm (NSGA) [18]. On the basis of absorbing the excellent searching mechanism of the equilibrium optimization algorithm and a novel mutation operator proposed in this work, this paper presented a disruption-based multiobjective equilibrium optimization algorithm (DMOEOA), which is able to handle multiobjective optimization problems. E introduction of equilibrium optimization operator and layered disruption method is presented in Section 3.Section 4 provides experimental results and analysis of DMOEOA on benchmark functions with five multiobjective optimization algorithms.
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