The fusion–fission optimization (FuFiO) algorithm

Abstract

Fusion–Fission Optimization (FuFiO) is proposed as a new metaheuristic algorithm that simulates the tendency of nuclei to increase their binding energy and achieve higher levels of stability. In this algorithm, nuclei are divided into two groups, namely stable and unstable. Each nucleus can interact with other nuclei using three different types of nuclear reactions, including fusion, fission, and β-decay. These reactions establish the stabilization process of unstable nuclei through which they gradually turn into stable nuclei. A set of 120 mathematical benchmark test functions are selected to evaluate the performance of the proposed algorithm. The results of the FuFiO algorithm and its related non-parametric statistical tests are compared with those of other metaheuristic algorithms to make a valid judgment. Furthermore, as some highly-complicated problems, the test functions of two recent Competitions on Evolutionary Computation, namely CEC-2017 and CEC-2019, are solved and analyzed. The obtained results show that the FuFiO algorithm is superior to the other metaheuristic algorithms in most of the examined cases.

A multi‐objective optimization approach to designing window and shading systems considering building energy consumption and occupant comfort

“…Optimization is the process of finding the best solution to a problem among a set of alternative solutions under some predefined conditions. Optimization problems arise in all branches of engineering including electrical, [65][66][67][68][69][70][71][72][73][74][75] mechanical, [76][77][78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93] and structural [94][95][96][97][98][99][100][101][102] engineering. Due to the simultaneous pursuit of several goals, often with conflicting outcomes, multi-objective optimization problems are generally more complex than single-objective problems.…”

An Improved Sparrow Search Algorithm and Its Application in HIFU Sound Field

“…Using ( 2) or ( 12) to update the scroungers' position; (10) end for (11) for i � 1: SD do (12) Using equation ( 3) to update the scouters' position; (13) end for (14) for i � 1: N do (15) if the new position is better than the previous position then (16) Using the new position to update the previous position; (17) end if (18) if the new position is better than the optimal position then (19) Using the new position to update the optimal position; (20) end if (21) end for (22) t � t + 1 (23) end while (24) return f best , X best ALGORITHM 1: Te improved sparrow search algorithm. Computational Intelligence and Neuroscience Equation ( 13) can be expressed as a matrix as follows:…”

“…After obtaining u N , the sound pressure at any point in the sound feld can be calculated by equation (13).…”

“…Tese algorithms have been applied to many optimization problems and show excellent performance. In recent years, more and more meta-heuristic algorithms have been proposed, such as the moth search algorithm (MSA) [8], harris hawks optimization (HHO) [9], sparrow search algorithm (SSA) [10], slime mould algorithm (SMA) [11], social network search (SNS) [12], and fusionfssion optimization (FuFiO) [13].…”

A swarm optimizer with modified feasible-based mechanism for optimum structure in steel industry

“…In the past few decades, many meta-heuristic methods have been developed [5][6][7][8][9][10][11][12][13] and applied for the optimum design of structures. Pezeshk et al [14] performed the optimal design of plane steel frames using the genetic algorithms (GA) and later in the other studies, it has been utilized to design steel frame structures [15][16][17].…”