The Applications of NP-hardness optimizations problem

Alridha, Ahmed; Salman, Abbas Musleh; Al-Jilawi, Ahmed Sabah

doi:10.1088/1742-6596/1818/1/012179

Cited by 8 publications

(2 citation statements)

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“…The shape of this goal function is crucial in order to provide appropriate solutions for guiding a system to a desired state or trajectory. [2,3,14,15] The most frequently used objective statements refer to economic, safety, operability, or environmental objectives.…”

Section: Dynamic Control Objectivementioning

confidence: 99%

Dynamic optimization technique with differential equations

Marjan¹,

Al-Jilawi

2022

ijhs

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One of the primary objectives of the problem is to reduce the cost of fuel consumed by generator units, and in recent years, we have developed new models to accomplish this goal. The first model problem represents a load following scenario, which is a common occurrence in power grids. The optimizer's objective is to balance demand and supply in response to fluctuating demand dynamics. At two different stages of the production process, a single producer tries to satisfy two constrained objectives in this model problem 2. By replacing a cogeneration system with n = 2 generators that generate (1) electricity and (2) heat in response to variations in demand, Model II improves on Model I. Using a n = 3 system with two products, three products, and three distinct order profiles, the third model issue builds on the prior two. The primary product remains unchanged from the preceding standard, and the slope rate is limited to the production of the two primary products (for example, electricity and heat). To create a third product (for example, a solid oxide electrolysis cell), the first two products are combined (for example, hydrogen).

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Section: Dynamic Control Objectivementioning

confidence: 99%

Dynamic optimization technique with differential equations

Marjan¹,

Al-Jilawi

2022

ijhs

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“…BN structure learning is challenging because the search space has a superexponential character [11,12]. This characteristic makes the structural learning of BN an NP-hard [13] problem [11]. Three different kinds of methods can achieve structural learning: constraint-based (CB) methods, search and score methods (also called score-based (SB)), and hybrid methods, that combine the two previous methods [10,14,15].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Network Structural Learning Using Adaptive Genetic Algorithm with Varying Population Size

Rodrigues Mendes Ribeiro,

Dias Maciel

2023

MAKE

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A Bayesian network (BN) is a probabilistic graphical model that can model complex and nonlinear relationships. Its structural learning from data is an NP-hard problem because of its search-space size. One method to perform structural learning is a search and score approach, which uses a search algorithm and structural score. A study comparing 15 algorithms showed that hill climbing (HC) and tabu search (TABU) performed the best overall on the tests. This work performs a deeper analysis of the application of the adaptive genetic algorithm with varying population size (AGAVaPS) on the BN structural learning problem, which a preliminary test showed that it had the potential to perform well on. AGAVaPS is a genetic algorithm that uses the concept of life, where each solution is in the population for a number of iterations. Each individual also has its own mutation rate, and there is a small probability of undergoing mutation twice. Parameter analysis of AGAVaPS in BN structural leaning was performed. Also, AGAVaPS was compared to HC and TABU for six literature datasets considering F1 score, structural Hamming distance (SHD), balanced scoring function (BSF), Bayesian information criterion (BIC), and execution time. HC and TABU performed basically the same for all the tests made. AGAVaPS performed better than the other algorithms for F1 score, SHD, and BIC, showing that it can perform well and is a good choice for BN structural learning.

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