Titlebook: Constraint Handling in Metaheuristics and Applications; Anand J. Kulkarni,Efrén Mezura-Montes,Ganesh Krish Book 2021 The Editor(s) (if app

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https://doi.org/10.1007/978-3-319-08057-4ing constraints in optimization problems. Metaheuristic algorithms can be categorized over various classes for separating them between different searching patterns and describe how the algorithm copy a specific phenomenal performance in the search area, diverse classification explored. The main focu

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https://doi.org/10.1007/978-3-319-08213-4tances. The rules are used to automatically switch between constraint handling techniques during PSO search. The switching is based on landscape information collected from the particles during search and requires no additional sampling or function evaluations. Results show that the proposed approach

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Mehmet Tevfik Dorak MD, PhD,Yusuf Yazici MDon tools are based initially on the Genetic Algorithm (GA) with two different selection strategies, namely stochastic and tournament selections. And secondly, on the Hybrid Simulated Annealing Genetic Algorithm (SAGA). The integration target of the simulated annealing (SA) based local search strateg

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https://doi.org/10.1007/978-3-319-08374-2in terms of the IC chips heat input (q.), PCM volume content (v.), and IC chips size (δ). The optimal temperature of the configuration is identified using the GA-based constrained optimization strategy. A sensitivity analysis is carried out to study the effect of the constraints on the optimal tempe

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The Find-Fix-Finish-Exploit-Analyze (F3EA) Meta-Heuristic Algorithm with an Extended Constraint Hancal optimization which develops new selection operators, a new parameter-free mutation operator, and a local search operator. The algorithm takes the surface of the objective function as the battleground and mimics the F3EA targeting process of object or installation selection for destruction in the