2020
DOI: 10.3390/math8040546
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Hybridization of Multi-Objective Deterministic Particle Swarm with Derivative-Free Local Searches

Abstract: The paper presents a multi-objective derivative-free and deterministic global/local hybrid algorithm for the efficient and effective solution of simulation-based design optimization (SBDO) problems. The objective is to show how the hybridization of two multi-objective derivative-free global and local algorithms achieves better performance than the separate use of the two algorithms in solving specific SBDO problems for hull-form design. The proposed method belongs to the class of memetic algorithms, where the … Show more

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Cited by 18 publications
(10 citation statements)
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“…According to a detailed review by Riquelme et al, the hypervolume is the most used metric in literature [75] and thus, it is the measure that is considered in this paper. It measures proximity and diversity at the same time and the higher its value, the better the approximation (indicative of better spread and convergence of solutions) [76].…”
Section: Performance Assessment With Hypervolume Metricmentioning
confidence: 99%
See 1 more Smart Citation
“…According to a detailed review by Riquelme et al, the hypervolume is the most used metric in literature [75] and thus, it is the measure that is considered in this paper. It measures proximity and diversity at the same time and the higher its value, the better the approximation (indicative of better spread and convergence of solutions) [76].…”
Section: Performance Assessment With Hypervolume Metricmentioning
confidence: 99%
“…Given the Pareto front approximation S and a reference point r ∈ R m s.t. ∀z ∈ S, z≺r, hypervolume indicator is given by [76]:…”
Section: Performance Assessment With Hypervolume Metricmentioning
confidence: 99%
“…Meanwhile, in [25], the hybridization of two multi-objective derivative-free global and local algorithms is presented. In this proposal, the global exploration capability of the deterministic MPOS is enhanced via the local search accuracy of a derivative-free line-search method (multi-objective).…”
Section: Multi-objective Particle Swarm Optimizationmentioning
confidence: 99%
“…(3) Efficient optimisation algorithm. Pellegrini et al [22] proposed a multi-objective derivative-free and deterministic global/local hybrid algorithm as an efficient and effective solution to SBDO problems. Tezdogan et al [23] proposed a hybrid algorithm to solve the complicated nonlinear optimisation problem of fishing boat.…”
Section: Introductionmentioning
confidence: 99%