scholarly journals Pareto Local Optima of Multiobjective NK-Landscapes with Correlated Objectives

2011 ◽  
pp. 226-237 ◽  
Author(s):  
Sébastien Verel ◽  
Arnaud Liefooghe ◽  
Laetitia Jourdan ◽  
Clarisse Dhaenens
Keyword(s):  
Local Optima ◽  
Nk Landscapes

scholarly journals Local Optima Networks of NK Landscapes With Neutrality

2011 ◽  
Vol 15 (6) ◽  
pp. 783-797 ◽  
Author(s):  
Sébastien Verel ◽  
Gabriela Ochoa ◽  
Marco Tomassini
Keyword(s):  
Local Optima ◽  
Local Optima Networks ◽  
Nk Landscapes

Gray Box Optimization for Mk Landscapes (NK Landscapes and MAX-kSAT)

2016 ◽  
Vol 24 (3) ◽  
pp. 491-519 ◽  
Author(s):  
L. Darrell Whitley ◽  
Francisco Chicano ◽  
Brian W. Goldman
Keyword(s):  
Dynamic Programming ◽  
Real World ◽  
Optimization Problems ◽  
Test Problems ◽  
Brute Force ◽  
Local Optima ◽  
Separable Problems ◽  
Boolean Optimization ◽  
Nk Landscapes ◽  
Real World Problems

This article investigates Gray Box Optimization for pseudo-Boolean optimization problems composed of M subfunctions, where each subfunction accepts at most k variables. We will refer to these as Mk Landscapes. In Gray Box Optimization, the optimizer is given access to the set of M subfunctions. We prove Gray Box Optimization can efficiently compute hyperplane averages to solve non-deceptive problems in [Formula: see text] time. Bounded separable problems are also solved in [Formula: see text] time. As a result, Gray Box Optimization is able to solve many commonly used problems from the evolutional computation literature in [Formula: see text] evaluations. We also introduce a more general class of Mk Landscapes that can be solved using dynamic programming and discuss properties of these functions. For certain type of problems Gray Box Optimization makes it possible to enumerate all local optima faster than brute force methods. We also provide evidence that randomly generated test problems are far less structured than those found in real-world problems.

Hybrid optimization strategy beyond local optima in aerospace panel designs

AIAA Journal ◽  
1999 ◽  
Vol 37 ◽  
pp. 588-593
Author(s):  
K. L. Chan ◽  
David Kennedy ◽  
Fred W. Williams
Keyword(s):  
Hybrid Optimization ◽  
Optimization Strategy ◽  
Local Optima

Hybrid optimisation strategy beyond local optima in aerospace panel designs

1998 ◽  
Author(s):  
K. Chan ◽  
D. Kennedy ◽  
F. Williams
Keyword(s):  
Local Optima

Feature Selection for Histopathological Image Classification using levy Flight Salp Swarm Optimizer

2019 ◽  
Vol 12 (4) ◽  
pp. 329-337 ◽  
Author(s):  
Venubabu Rachapudi ◽  
Golagani Lavanya Devi
Keyword(s):  
Feature Selection ◽  
Image Classification ◽  
Feature Selection Method ◽  
Selection Method ◽  
Lévy Flight ◽  
Levy Flight ◽  
Local Optima ◽  
Histopathological Image ◽  
Surf Features ◽  
Histopathological Image Classification

Background: An efficient feature selection method for Histopathological image classification plays an important role to eliminate irrelevant and redundant features. Therefore, this paper proposes a new levy flight salp swarm optimizer based feature selection method. Methods: The proposed levy flight salp swarm optimizer based feature selection method uses the levy flight steps for each follower salp to deviate them from local optima. The best solution returns the relevant and non-redundant features, which are fed to different classifiers for efficient and robust image classification. Results: The efficiency of the proposed levy flight salp swarm optimizer has been verified on 20 benchmark functions. The anticipated scheme beats the other considered meta-heuristic approaches. Furthermore, the anticipated feature selection method has shown better reduction in SURF features than other considered methods and performed well for histopathological image classification. Conclusion: This paper proposes an efficient levy flight salp Swarm Optimizer by modifying the step size of follower salp. The proposed modification reduces the chances of sticking into local optima. Furthermore, levy flight salp Swarm Optimizer has been utilized in the selection of optimum features from SURF features for the histopathological image classification. The simulation results validate that proposed method provides optimal values and high classification performance in comparison to other methods.

scholarly journals Solving knapsack problems using a binary gaining sharing knowledge-based optimization algorithm

2021 ◽  
Author(s):  
Prachi Agrawal ◽  
Talari Ganesh ◽  
Ali Wagdy Mohamed
Keyword(s):  
Life Span ◽  
Population Size ◽  
Linear Function ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Knapsack Problems ◽  
Local Optima ◽  
Knowledge Based ◽  
Two Stages

AbstractThis article proposes a novel binary version of recently developed Gaining Sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. A binary version of GSK named novel binary Gaining Sharing knowledge-based optimization algorithm (NBGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable NBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. Moreover, to enhance the performance of NBGSK and prevent the solutions from trapping into local optima, NBGSK with population size reduction (PR-NBGSK) is introduced. It decreases the population size gradually with a linear function. The proposed NBGSK and PR-NBGSK applied to set of knapsack instances with small and large dimensions, which shows that NBGSK and PR-NBGSK are more efficient and effective in terms of convergence, robustness, and accuracy.

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