Performance metrics in multi-objective optimization

Riquelme, Nery; Lücken, Christian von; Barán, Benjamín

doi:10.1109/clei.2015.7360024

Cited by 301 publications

(168 citation statements)

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“…The presented multiobjective quantum algorithm, called MO-GAS, is a natural extension of previous quantum algorithms for single-objective optimization based on Grover's search method. The experimental results of this work suggests that MOGAS (considering both types of oracles) was not only an effective approach for multiobjective optimization problems, but it was also efficient as was observed when MOGAS was compared against NSGA-II, which is one of the most cited multiobjective optimization algorithms [8]. In most of the studied cases, MOGAS obtained better or equal results in average after comparing it against NSGA-II for the same number of executions especially with respect to the oracle based on the boolean function h 1 ; in regard of h 2 , the results presented in this work are almost equal compared to NSGA-II.…”

Section: Discussionmentioning

confidence: 88%

“…To verify the effectiveness of the proposed algorithm, we have tested it by means of simulations against one of the most cited optimization algorithms for multiobjective problems, the Non-dominated Sorting Genetic Algorithm -version two [7], [8] known as NSGA-II. The tests were made considering some biobjective problems based on the well known ZDT test suite [10] and on randomly generated instances.…”

Section: Resultsmentioning

confidence: 99%

“…Where the algorithm consultation is exactly to a performed Grover search with regard to r k iterations on F(x) considering a threshold Y k , and denoting the outputs by x and Y respectively. The hypervolume was used as the metric for the comparison of the results, considering that it is the most used comparison metric in multiobjective optimization [8]. The hypervolume is an indicator used in the multiobjective optimization of evolutionary algorithms to evaluate the performance of the search, which was proposed by Zitzler and Thiele [11].…”

Section: Resultsmentioning

confidence: 99%

“…The objectives are frequently in conflict, and therefore, there may exists several "optimal" solutions. The set of optimal solutions is known as a Pareto-optimal set, where solutions provide the best compromise relations between the objective functions considering the entire feasible decision space [7], [8].…”

Section: Multiobjective Optimizationmentioning

confidence: 99%

See 3 more Smart Citations

Comparison of two types of Quantum Oracles based on Grover’s Adaptative Search Algorithm for Multiobjective Optimization Problems

Fogel

Barán

Villagra

2017

Annals of Computer Science and Information Systems

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Abstract-Quantum Computing is a field of study in computer science based on the laws of quantum physics. Quantum computing is an attractive subject considering that quantum algorithms proved to be more efficient than classical algorithms and the advent of large-scale quantum computation. In particular, Grover's search algorithm is a quantum algorithm that is asymptotically faster than any classical search algorithm and it is relevant for the design of fast optimization algorithms. This article proposes two algorithms based on Grover's adaptative search for biobjective optimization problems where access to the objective functions is given via two different quantum oracles. The proposed algorithms, considering both types of oracles, are compared against NSGA-II, a highly cited multiobjective optimization evolutionary algorithm. Experimental evidence suggests that the quantum optimization methods proposed in this work are at least as effective as NSGA-II in average, considering an equal number of executions. Experimental results showed which oracle required less iterations for similar effectiveness.

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Section: Discussionmentioning

confidence: 88%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Multiobjective Optimizationmentioning

confidence: 99%

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Comparison of two types of Quantum Oracles based on Grover’s Adaptative Search Algorithm for Multiobjective Optimization Problems

Fogel

Barán

Villagra

2017

Annals of Computer Science and Information Systems

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“…We use SP to test the second issue that mentioned previously. SP can be measured by the following equation [52]:…”

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confidence: 99%

Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP)

2017

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Abstract:In this paper, we propose an extended multi-objective version of single objective optimization algorithm called sperm swarm optimization algorithm. The proposed multi-objective optimization algorithm based on sperm fertilization procedure (MOSFP) operates based on Pareto dominance and a crowding factor, that crowd and filter out the list of the best sperms (global best values). We divide the sperm swarm into three equal parts, after that, different types of turbulence (mutation) operators are applied on these parts, such as uniform mutation, non-uniform mutation, and without any mutation. Our algorithm is compared against three well-known algorithms in the field of optimization. These algorithms are NSGA-II, SPEA2, and OMOPSO. These algorithms are compared using a very popular benchmark function suites called Zitzler-Deb-Thiele (ZDT) and Walking-Fish-Group (WFG). We also adopt three quality metrics to compare the convergence, accuracy, and diversity of these algorithms, including, inverted generational distance (IGD), spread (SP), and epsilon (∈). The experimental results show that the performance of the proposed MOSFP is highly competitive, which outperformed OMOPSO in solving problems such as ZDT3, WFG5, and WFG8. In addition, the proposed MOSFP outperformed both of NSGA-II or SPEA2 algorithms in solving all the problems.

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Stochastic Multiobjective Optimization: A Descent Algorithm

Mercier

Poirion

2021

Mechanical Engineering Under Uncertainties

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Performance metrics in multi-objective optimization

Cited by 301 publications

References 48 publications

Comparison of two types of Quantum Oracles based on Grover’s Adaptative Search Algorithm for Multiobjective Optimization Problems

Comparison of two types of Quantum Oracles based on Grover’s Adaptative Search Algorithm for Multiobjective Optimization Problems

Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP)

Stochastic Multiobjective Optimization: A Descent Algorithm

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