An adaptive nonmonotone line search for multiobjective optimization problems

Ghalavand, Nasim; Khorram, Esmaile; Morovati, Vahid

doi:10.1016/j.cor.2021.105506

Cited by 9 publications

(3 citation statements)

References 30 publications

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“…The best value is defined as the squirrel on the pecan tree, the three of the next best values are the squirrels on the oak trees, and the rest values are the squirrels on the normal trees. While (the stopping criterion is not satisfied) do For t = 1 to n Update flying squirrel locations which are on oak trees and moving towards pecan trees using Equation (3) Update flying squirrel locations which are on normal trees and moving towards oak trees using Equation (4) Evaluate the fitness of each flying squirrel's location. Update flying squirrel locations which are on normal trees and moving towards pecan trees using Equation (5) Evaluate the fitness of each flying squirrel location.…”

Section: Beginmentioning

confidence: 99%

“…Over time, scholars have developed many methods to deal with optimization problems. These methods include the Gradient Descent Optimizer [1,2], Line Search Algorithm [3,4], and Trust Region Algorithm [5,6], among others. However, as problems become increasingly complex, traditional methods face challenges when confronted with optimization problems that involve intricate constraints and complicated calculation processes.…”

Section: Introductionmentioning

confidence: 99%

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FSSSA: A Fuzzy Squirrel Search Algorithm Based on Wide-Area Search for Numerical and Engineering Optimization Problems

Chen,

Zhao,

2023

Mathematics

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The Squirrel Search Algorithm (SSA) is widely used due to its simple structure and efficient search ability. However, SSA exhibits relatively slow convergence speed and imbalanced exploration and exploitation. To address these limitations, this paper proposes a fuzzy squirrel search algorithm based on a wide-area search mechanism named FSSSA. The fuzzy inference system and sine cosine mutation are employed to enhance the convergence speed. The wide-area search mechanism is introduced to achieve a better balance between exploration and exploitation, as well as improve the convergence accuracy. To evaluate the effectiveness of the proposed strategies, FSSSA is compared with SSA on 24 diverse benchmark functions, using four evaluation indexes: convergence speed, convergence accuracy, balance and diversity, and non-parametric test. The experimental results demonstrate that FSSSA outperforms SSA in all four indexes. Furthermore, a comparison with eight metaheuristic algorithms is conducted to illustrate the optimization performance of FSSSA. The results indicate that FSSSA exhibits excellent convergence speed and overall performance. Additionally, FSSSA is applied to four engineering problems, and experimental verification confirms that it maintains superior performance in realistic optimization problems, thus demonstrating its practicality.

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Section: Beginmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FSSSA: A Fuzzy Squirrel Search Algorithm Based on Wide-Area Search for Numerical and Engineering Optimization Problems

Chen,

Zhao,

2023

Mathematics

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“…For an introduction to multi‐objective programming see References 6‐8. For more some recent new works and surveys of recent developments on multi‐objective optimization problems, one can also see References 9‐18.…”

Section: Introductionmentioning

confidence: 99%

Utilizing a projection neural network to convex quadratic multi‐objective programming problems

Jahangiri

Nazemi

2023

Adaptive Control & Signal

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Summary In this paper, we propose a projection neural network model for solving convex quadratic multi‐objective optimization problem (CQMOP). The CQMOP is first converted into an equivalent convex nonlinear programming problem by the means of the weighted sum method, where the Pareto optimal solutions are calculated via different values of weights. A neural network model is then constructed for solving the obtained convex problem. It is shown that the presented neural network is stable in the sense of Lyapunov and is globally convergent. Simulation results are given to illustrate the global convergence and performance of the suggested model. Both theoretical and numerical approaches are studied. Numerical results are in good agreement with the proved theoretical concepts.

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Solving general convex quadratic multi-objective optimization problems via a projection neurodynamic model

Jahangiri,

Nazemi

2023

Cogn Neurodyn

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An adaptive nonmonotone line search for multiobjective optimization problems

Cited by 9 publications

References 30 publications

FSSSA: A Fuzzy Squirrel Search Algorithm Based on Wide-Area Search for Numerical and Engineering Optimization Problems

FSSSA: A Fuzzy Squirrel Search Algorithm Based on Wide-Area Search for Numerical and Engineering Optimization Problems

Utilizing a projection neural network to convex quadratic multi‐objective programming problems

Solving general convex quadratic multi-objective optimization problems via a projection neurodynamic model

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