An active-learning probabilistic neural network for feasibility classification of constrained engineering optimization problems
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DOI: 10.1007/s00366-021-01441-4
Abstract: Constrained optimization problems (COPs) with multiple computational expensive constraints are commonly encountered in simulation-based engineering designs. During the optimization process, the feasibility analysis of the intermediate solutions depends on the computational simulations will be computationally… read more here.
Keywords: engineering; classification; active learning; optimization ... See more keywords
An improved elephant herding optimization for global optimization problems
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DOI: 10.1007/s00366-021-01471-y
Abstract: This study proposes a modified Elephant Herding Optimization algorithm to enhance the capability of a classical algorithm for convalescent convergence rate and precision to solve global optimization problems. The proposed Improved Elephant Herding Optimization (IEHO)… read more here.
Keywords: optimization; improved elephant; optimization problems; global optimization ... See more keywords
An improved Chaotic Harris Hawks Optimizer for solving numerical and engineering optimization problems
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DOI: 10.1007/s00366-021-01487-4
Abstract: Harris Hawk’s Optimizer (HHO) is a recently developed meta-heuristics search algorithm with inherent capability to explore global minima and maxima. However, the local search of the basic HHO algorithm is sluggish and has slow convergence… read more here.
Keywords: improved chaotic; optimization problems; engineering; chaotic harris ... See more keywords
Objective reduction for many-objective optimization problems using objective subspace extraction
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DOI: 10.1007/s00500-017-2498-6
Abstract: Multi-objective evolutionary algorithms (MOEAs) have shown their effectiveness in exploring a well converged and diversified approximation set for multi-objective optimization problems (MOPs) with 2 and 3 objectives. However, most of them perform poorly when tackling… read more here.
Keywords: many objective; objective optimization; optimization; optimization problems ... See more keywords
An efficient neural network for solving convex optimization problems with a nonlinear complementarity problem function
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DOI: 10.1007/s00500-019-04189-8
Abstract: In this paper, we present a one-layer recurrent neural network (NN) for solving convex optimization problems by using the Mangasarian and Solodov (MS) implicit Lagrangian function. In this paper by using Krush–Kuhn–Tucker conditions and MS… read more here.
Keywords: convex optimization; neural network; problem; optimization problems ... See more keywords
A reformative teaching–learning-based optimization algorithm for solving numerical and engineering design optimization problems
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DOI: 10.1007/s00500-020-04918-4
Abstract: Teaching–learning-based optimization (TLBO) algorithm, which simulates the process of teaching–learning in the classroom, has been studied by many researchers, and a number of experiments have shown that it has great performance in solving optimization problems.… read more here.
Keywords: based optimization; teaching learning; optimization; engineering design ... See more keywords
Volcano eruption algorithm for solving optimization problems
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DOI: 10.1007/s00521-020-05124-x
Abstract: Meta-heuristic algorithms have been proposed to solve several optimization problems in different research areas due to their unique attractive features. Traditionally, heuristic approaches are designed separately for discrete and continuous problems. This paper leverages the… read more here.
Keywords: optimization problems; algorithm solving; volcano eruption; optimization ... See more keywords
Enhanced migrating birds optimization algorithm for optimization problems in different domains
Sign Up to like & getrecommendations! Published in 2024 at "Annals of Operations Research"
DOI: 10.1007/s10479-024-05992-9
Abstract: Migrating birds optimization algorithm is a promising metaheuristic algorithm recently introduced to the optimization community. In this study, we propose a superior version of the migrating birds optimization algorithm by hybridizing it with the simulated… read more here.
Keywords: optimization problems; migrating birds; birds optimization; optimization algorithm ... See more keywords
Kernel Technology to Solve Discrete Optimization Problems
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DOI: 10.1007/s10559-017-9990-y
Abstract: The kernel technology is proposed to solve discrete optimization problems. It forms solution kernel and allows efficient stochastic perturbations of this solution in iterative schemes. Comparative analysis of the two versions of the new algorithm… read more here.
Keywords: kernel technology; discrete optimization; technology; solve discrete ... See more keywords
Fragmentary Structures in Discrete Optimization Problems
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DOI: 10.1007/s10559-017-9995-6
Abstract: The paper considers a combinatorial object (a fragmentary structure) and investigates the properties of this object. It is shown that a number of discrete optimization problems can be considered as optimization problems on a fragmentary… read more here.
Keywords: fragmentary structure; fragmentary structures; discrete optimization; problems fragmentary ... See more keywords
An efficient improved exponential distribution optimizer: application to the global, engineering and combinatorial optimization problems
Sign Up to like & getrecommendations! Published in 2024 at "Cluster Computing"
DOI: 10.1007/s10586-024-04382-x
Abstract: Population-based meta-heuristic optimization algorithms play a vital role in addressing optimization problems. Nowadays, exponential distribution optimizer (EDO) can be considered to be one of the most recent among these algorithms. Although it has achieved many… read more here.
Keywords: optimization problems; distribution optimizer; optimization; global engineering ... See more keywords