Nuclear Fission-Nuclear Fusion algorithm for global optimization: a modified Big Bang-Big Crunch algorithm

Date

2020-04-01

Author

YALÇIN, YAĞIZER
Pekcan, Onur

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

312
views

0
downloads

This study introduces a derivative of the well-known optimization algorithm, Big Bang-Big Crunch (BB-BC), named Nuclear Fission-Nuclear Fusion-based BB-BC, simply referred to as N2F. Broadly preferred in the engineering optimization community, BB-BC provides accurate solutions with reasonably fast convergence rates for many engineering problems. Regardless, the algorithm often suffers from stagnation issues. More specifically, for some problems, BB-BC either converges prematurely or exploits the promising regions inefficiently, both of which prevent obtaining the optimal solution. To overcome such problems, N2F algorithm is proposed, inspired by two major phenomena of nuclear physics: fission and fusion reactions. In N2F, two concepts named "Nuclear Fission" and "Nuclear Fusion" are introduced, replacing the "Big Bang" and "Big Crunch" phases of BB-BC, respectively. With the "Nuclear Fission" phase represented through a parameter named amplification factor, premature convergence issues are eliminated to a great extent. Meanwhile, convergence rate and exploitation capability of the algorithm are enhanced largely through a precision control parameter named magnification factor, in the "Nuclear Fusion" phase. The performance of N2F algorithm is investigated through unconstrained test functions and compared with the conventional BB-BC and other metaheuristics including genetic algorithm, Particle Swarm Optimization (PSO), Artificial Bee Colony Optimization (ABC), Drone Squadron Optimization (DSO) and Salp Swarm Algorithm (SSA). Then, further analyses are performed with constrained design benchmarks, validating the applicability of N2F to engineering problems. With superior statistical performance compared to BB-BC, GA, PSO, ABC, DSO and SSA in unconstrained problems and improved results with respect to the literature studies, N2F is proven to be an efficient and robust optimization algorithm.

Subject Keywords

Software, Artificial Intelligence

URI

https://hdl.handle.net/11511/48444

Journal

NEURAL COMPUTING & APPLICATIONS

DOI

https://doi.org/10.1007/s00521-018-3907-1

Collections

Department of Civil Engineering, Article

Suggestions

OpenMETU
Core

Improving reinforcement learning by using sequence trees Girgin, Sertan; Polat, Faruk; Alhajj, Reda (Springer Science and Business Media LLC, 2010-12-01) This paper proposes a novel approach to discover options in the form of stochastic conditionally terminating sequences; it shows how such sequences can be integrated into the reinforcement learning framework to improve the learning performance. The method utilizes stored histories of possible optimal policies and constructs a specialized tree structure during the learning process. The constructed tree facilitates the process of identifying frequently used action sequences together with states that are visit...
LinGraph: a graph-based automated planner for concurrent task planning based on linear logic Kortik, Sitar; Saranlı, Uluç (Springer Science and Business Media LLC, 2017-10-01) In this paper, we introduce an automated planner for deterministic, concurrent domains, formulated as a graph-based theorem prover for a propositional fragment of intuitionistic linear logic, relying on the previously established connection between intuitionistic linear logic and planning problems. The new graph-based theorem prover we introduce improves planning performance by reducing proof permutations that are irrelevant to planning problems particularly in the presence of large numbers of objects and a...
Multi-objective multi-item fixed-charge solid transportation problem under twofold uncertainty Roy, Sankar Kumar; Midya, Sudipta; Weber, Gerhard Wilhelm (Springer Science and Business Media LLC, 2019-12-01) In this paper, we investigate a multi-objective multi-item fixed-charge solid transportation problem (MOMIFCSTP) with fuzzy-rough variables as coefficients of the objective functions and of the constraints. The main focus of the paper is to analyze MOMIFCSTP under a fuzzy-rough environment for a transporting system. In practical situations, the parameters of a MOMIFCSTP are imprecise in nature, due to several uncontrollable factors. For these reasons, we introduce the fuzzy-rough variables in MOMIFCSTP to t...
A Hopfield neural network with multi-compartmental activation Akhmet, Marat (Springer Science and Business Media LLC, 2018-05-01) The Hopfield network is a form of recurrent artificial neural network. To satisfy demands of artificial neural networks and brain activity, the networks are needed to be modified in different ways. Accordingly, it is the first time that, in our paper, a Hopfield neural network with piecewise constant argument of generalized type and constant delay is considered. To insert both types of the arguments, a multi-compartmental activation function is utilized. For the analysis of the problem, we have applied the ...
Resampling approach for cluster model selection Volkovich, Z.; Barzily, Z.; Weber, Gerhard Wilhelm; Toledano-Kitai, D.; Avros, R. (Springer Science and Business Media LLC, 2011-10-01) In cluster analysis, selecting the number of clusters is an "ill-posed" problem of crucial importance. In this paper we propose a re-sampling method for assessing cluster stability. Our model suggests that samples' occurrences in clusters can be considered as realizations of the same random variable in the case of the "true" number of clusters. Thus, similarity between different cluster solutions is measured by means of compound and simple probability metrics. Compound criteria result in validation rules em...

Citation Formats

Y. YALÇIN and O. Pekcan, “Nuclear Fission-Nuclear Fusion algorithm for global optimization: a modified Big Bang-Big Crunch algorithm,” NEURAL COMPUTING & APPLICATIONS, pp. 2751–2783, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/48444.