True global optimality of the pressure vessel design problem: a benchmark for bio-inspired optimisation algorithms

Yang, Xin-She; Huyck, Christian R.; Karamanoglu, Mehmet; Khan, Nawaz

doi:10.1504/ijbic.2013.058910

Cited by 32 publications

(11 citation statements)

References 46 publications

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“…From the results presented in Table 22, it can be seen that there are only four methods (rank-iMDDE, NDE, COMDE and UFA) that are able to solve this problem in all runs. There is some error related to the best result reported by the AFA in (Baykasolu and Ozsoydan 2015), since it is less than the optimal solution 6059.714335048436 which is proved by a detailed mathematical analysis in Yang et al (2013b). Among all compared approaches, the UFA has obtained the smallest standard deviation value, with the exception of the COMDE.…”

Section: Results For Pressure Vessel Design Problemmentioning

confidence: 94%

An upgraded firefly algorithm with feasibility-based rules for constrained engineering optimization problems

Brajević

Ignjatović

2018

J Intell Manuf

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The firefly algorithm (FA) has become one of the most prominent swarm intelligence methods due to its efficiency in solving a wide range of various real-world problems. In this paper, an upgraded firefly algorithm (UFA) is proposed to further improve its performance in solving constrained engineering optimization problems. The main modifications of the basic algorithm are the incorporation of the logistic map and reduction scheme mechanism in order to perform fine adjustments of its control parameters, and employing a mutation operator in order to provide useful diversity in the population. Also, the proposed approach uses certain feasibility-based rules in order to guide the search to the feasible region of the search space, the improved scheme to handle the boundary constraints and the method for handling equality constraints. The UFA is tested on a set of 24 benchmark functions presented in CEC'2006 and nine widely used constrained engineering optimization problems. Comprehensive experimental results show that the overall performance of the UFA is superior to the FA and its recently proposed variants. Moreover, it achieves highly competitive results compared with other state-of-the-art metaheuristic techniques.

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Section: Results For Pressure Vessel Design Problemmentioning

confidence: 94%

An upgraded firefly algorithm with feasibility-based rules for constrained engineering optimization problems

Brajević

Ignjatović

2018

J Intell Manuf

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“…Ke et al (2016) applied fruit fly optimization algorithm (FOA) to the pressure vessel problem and compared it with the results of PSO and quantum artificial fish swarm algorithm. Yang et al (2013) performed a detailed mathematical study and stated that the global optimum is 6,059.714335048436. They found the same result with the Lagrange multiplier (LM) method.…”

Section: Hybrid Optimization Methodsmentioning

confidence: 99%

A hybrid ant colony–particle swarm optimization method (ACOPSO) for aerospace propulsion systems

Piskin

Baklacıoğlu

Turan

2021

AEAT

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Purpose The purpose of this paper is to introduce a hybrid, metaheuristic, multimodal and multi-objective optimization tool that is needed for aerospace propulsion engineering problems. Design/methodology/approach Multi-objective hybrid optimization code is integrated with various benchmark and test functions that are selected suitable to the difficulty level of the aero propulsion performance problems. Findings Ant colony and particle swarm optimization (ACOPSO) has performed satisfactorily with benchmark problems. Research limitations/implications ACOPSO is able to solve multi-objective and multimodal problems. Because every optimization problem has specific features, it is necessary to search their general behavior using other algorithms. Practical implications In addition to the optimization solving, ACOPSO enables an alternative methodology for turbine engine performance calculations by using generic components maps. The user is flexible for searching various effects of component designs along with the compressor and turbine maps. Originality/value A hybrid optimization code that has not been used before is introduced. It is targeted use is propulsion systems optimization and design such as Turboshaft or turbofan by preparing the necessary engine functions. A number of input parameters and objective functions can be modified accordingly.

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“…Using the same parameter settings as before, the results of 20 independent runs are summarized and compared in Table 4. In fact, all these algorithms can find the global optimal solution as found by Yang et al [47].…”

Section: Pressure Vessel Designmentioning

confidence: 99%

Multi-species Cuckoo Search Algorithm for Global Optimization

Yang¹,

Deb

Mishra

2018

Cogn Comput

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Background: Many optimization problems in science and engineering are highly nonlinear, and thus require sophisticated optimization techniques to solve. Traditional techniques such as gradient-based algorithms are mostly local search methods, and often struggle to cope with such challenging optimization problems. Recent trends tend to use nature-inspired optimization algorithms.Methods: This work extends the standard cuckoo search (CS) by using the successful features of the cuckoo-host co-evolution with multiple interacting species, and the proposed multi-species cuckoo search (MSCS) intends to mimic the multiple species of cuckoos that compete for the survival of the fittest, and they co-evolve with host species with solution vectors being encoded as position vectors. The proposed algorithm is then validated by 15 benchmark functions as well as five nonlinear, multimodal design case studies in practical applications.Results: Simulation results suggest that the proposed algorithm can be effective for finding optimal solutions and in this case all optimal solutions are achievable. The results for the test benchmarks are also compared with those obtained by other methods such as the standard cuckoo search and genetic algorithm, which demonstrated the efficiency of the present algorithm.Conclusions: Based on numerical experiments and case studies, we can conclude that the proposed algorithm can be more efficient in most cases, leading a potentially very effective tool for solving nonlinear optimization problems.

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True global optimality of the pressure vessel design problem: a benchmark for bio-inspired optimisation algorithms

Cited by 32 publications

References 46 publications

An upgraded firefly algorithm with feasibility-based rules for constrained engineering optimization problems

An upgraded firefly algorithm with feasibility-based rules for constrained engineering optimization problems

A hybrid ant colony–particle swarm optimization method (ACOPSO) for aerospace propulsion systems

Multi-species Cuckoo Search Algorithm for Global Optimization

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