Primary study on the stochastic spiral optimization algorithm

Cruz–Duarte, Jorge M.; Martin-Diaz, Ignacio; Muñoz‐Minjares, Jorge; Sanchez-Galindo, Luis A.; Aviña-Cervantes, Juan Gabriel; García-Pérez, Arturo; Correa-Cely, Rodrigo

doi:10.1109/ropec.2017.8261609

Cited by 13 publications

(7 citation statements)

References 14 publications

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“…The SV perturbators were designed conforming several works reported in the literature [32,33]. They were extracted from ten well-known metaheuristics: Random Search [34], Simulated Annelaing [35], Genetic Algorithm [36], Cuckoo Search [37], Differential Evolution [38], Particle Swarm Optimisation [39], Firefly Algorithm [40], Stochastic Spiral Optimisaiton Algorithm [41], Central Force Optimisation [42], and Gravitational Search Algorithm [43]. We also included the random sample because it is the most straightforward manner of performing a search in an arbitrary domain.…”

Section: Methodsmentioning

confidence: 99%

“…We extracted them from two well-known metaheuristics as PSO [39] and fa [40]. Lastly, the final three perturbators concern those from MHs that model trajectories and dynamics common in the classical mechanics, such as SOA [41], CFO [53], and GSA [43].…”

Section: Appendix A22 Population-based Fixed-dimension Perturbatorsmentioning

confidence: 99%

“…In this section, we detail the simple heuristics extracted from the following 10 wellknown MHs: Random Search (RS) [48], Simulated Annealing (SA) [3], Genetic Algorithm (GA) [49], Cuckoo Search (CS) [50], Differential Evolution (DE) [38], Particle Swarm Optimisation (PSO) [39,51], Firefly Algorithm (FA) [52], Stochastic Spiral Optimisation Algorithm (SSOA) [41], Central Force Optimisation (CFO) [53], and Gravitational Search Algorithm (GSA) [43]. These heuristics are presented and organised using the same nomenclature and terminology of Section 2.…”

Section: Abbreviationsmentioning

confidence: 99%

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Global Optimisation through Hyper-Heuristics: Unfolding Population-Based Metaheuristics

et al. 2021

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Optimisation has been with us since before the first humans opened their eyes to natural phenomena that inspire technological progress. Nowadays, it is quite hard to find a solver from the overpopulation of metaheuristics that properly deals with a given problem. This is even considered an additional problem. In this work, we propose a heuristic-based solver model for continuous optimisation problems by extending the existing concepts present in the literature. We name such solvers ‘unfolded’ metaheuristics (uMHs) since they comprise a heterogeneous sequence of simple heuristics obtained from delegating the control operator in the standard metaheuristic scheme to a high-level strategy. Therefore, we tackle the Metaheuristic Composition Optimisation Problem by tailoring a particular uMH that deals with a specific application. We prove the feasibility of this model via a two-fold experiment employing several continuous optimisation problems and a collection of diverse population-based operators with fixed dimensions from ten well-known metaheuristics in the literature. As a high-level strategy, we utilised a hyper-heuristic based on Simulated Annealing. Results demonstrate that our proposed approach represents a very reliable alternative with a low computational cost for tackling continuous optimisation problems with a tailored metaheuristic using a set of agents. We also study the implication of several parameters involved in the uMH model and their influence over the solver performance.

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Section: Methodsmentioning

confidence: 99%

Section: Appendix A22 Population-based Fixed-dimension Perturbatorsmentioning

confidence: 99%

Section: Abbreviationsmentioning

confidence: 99%

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Global Optimisation through Hyper-Heuristics: Unfolding Population-Based Metaheuristics

et al. 2021

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“…One of the significant drawbacks of this algorithm is the slow convergence. Therefore, the authors of [29][30][31] have proposed a stochastic SDO algorithm by incorporating some random disturbances at each searching point of the algorithm. Similarly, the authors of [32] have introduced the iterative SDO algorithm for analyzing the information on blurred images.…”

Section: Improved Versions Of Spiral Dynamics Optimization Algorithmmentioning

confidence: 99%

“…• Micro-channel heat sink [29,30]; • Automation of high-rise buildings [19]; • Planar, spatial truss structures [18]; • Pressure vessel design problems [38,50]; • Welded beam design problems [50].…”

Section: Mechanical Systems Optimizationmentioning

confidence: 99%

Recent Advances and Applications of Spiral Dynamics Optimization Algorithm: A Review

et al. 2022

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This paper comprehensively reviews the spiral dynamics optimization (SDO) algorithm and investigates its characteristics. SDO algorithm is one of the most straightforward physics-based optimization algorithms and is successfully applied in various broad fields. This paper describes the recent advances of the SDO algorithm, including its adaptive, improved, and hybrid approaches. The growth of the SDO algorithm and its application in various areas, theoretical analysis, and comparison with its preceding and other algorithms are also described in detail. A detailed description of different spiral paths, their characteristics, and the application of these spiral approaches in developing and improving other optimization algorithms are comprehensively presented. The review concludes the current works on the SDO algorithm, highlighting its shortcomings and suggesting possible future research perspectives.

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Formulation and application of quantum-inspired tidal firefly technique for multiple-objective mixed cost-effective emission dispatch

Bodha

Yadav

Mukherjee

2019

Neural Comput & Applic

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Primary study on the stochastic spiral optimization algorithm

Cited by 13 publications

References 14 publications

Global Optimisation through Hyper-Heuristics: Unfolding Population-Based Metaheuristics

Global Optimisation through Hyper-Heuristics: Unfolding Population-Based Metaheuristics

Recent Advances and Applications of Spiral Dynamics Optimization Algorithm: A Review

Formulation and application of quantum-inspired tidal firefly technique for multiple-objective mixed cost-effective emission dispatch

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