Knapsack Optimization Research Articles

Network Intrusion Detection Using Knapsack Optimization, Mutual Information Gain, and Machine Learning

Network Intrusion Detection Using Knapsack Optimization, Mutual Information Gain, and Machine Learning

A New Method for the Techno-Economic Analysis and the Identification of Expansion Strategies of Neutral-Temperature District Heating and Cooling Systems

Neutral-temperature district heating and cooling (NT-DHC) is a recent concept in the district heating sector. The current literature does not directly address the ability to create comprehensive master plans for NT-DHC systems and reliably model their performance. This research presents a new approach for the evaluation and planning of NT-DHC systems. The methodology involves the use of a knapsack optimization algorithm to perform a comprehensive analysis of the conditions that make the NT-DHC solution competitive against individual heating and cooling technologies. The algorithm determines the optimal combination of potential extensions that maximizes overall economic value. The results of a case study, which was conducted in Italy, show that NT-DHC is more suitable in dense urban areas, while air-to-water heat pumps are better suited for low heat density zones. This methodology aims to reduce the risks associated with energy demand and provide more certainty about which areas a network can expand into to be competitive. It is targeted at energy planners, utilities experts, energy engineers, and district heating experts who require assistance and guidance in the planning and early stages of designing a NT-DHC system. This method might enable pre-feasibility studies and preliminary design to determine the opportunities and limitations of a system of this kind from an economic and technological perspective.

BEET: Blockchain Enabled Energy Trading for E-Mobility Oriented Electric Vehicles

Renewable Energy Sources (RESs) are gaining considerable attention to reduce human dependence on fossil fuels and minimize harmful gases in our surroundings. Existing literature on energy trading focused on providing renewable energy to smart homes, smart buildings, and smart offices to fulfill their daily energy demands obtained from RESs. Besides, Electric Vehicles (EVs) use either power grid energy or a battery exchange mechanism to recharge their low EV batteries. The continuous use of power grids to recharge low EV batteries causes a significant load on power grids. Due to this, power grids are inadequate to fulfill the ever-increasing demands of EVs in the future. In this context, we propose a Blockchain Enabled Energy Trading (BEET) framework oriented EV charging. A system architecture of the BEET framework is presented to describe the functioning of each layer and its associated entities. We formulate an optimization problem that maximizes the revenue in the energy trading process using a knapsack optimization. Smart contracts are designed on the consortium blockchain network to sell and buy renewable energy to aggregators and from producers, respectively. Moreover, an EV charging mechanism is designed to intelligently allocate renewable energy to consumers at a low price. A comparative analysis is performed with state-of-the-art works in terms of charging price, revenue, throughput, and latency. The results indicate that the BEET framework outperforms compared to state-of-the-art works to address the renewable energy demand problem to realize E-mobility. It is clarified that the data considered in the experimental analysis were obtained from statistical simulations in realistic E-Mobility environment settings.

Not so smart? "Smart" drugs increase the level but decrease the quality of cognitive effort.

The efficacy of pharmaceutical cognitive enhancers in everyday complex tasks remains to be established. Using the knapsack optimization problem as a stylized representation of difficulty in tasks encountered in daily life, we discover that methylphenidate, dextroamphetamine, and modafinil cause knapsack value attained in the task to diminish significantly compared to placebo, even if the chance of finding the optimal solution (~50%) is not reduced significantly. Effort (decision time and number of steps taken to find a solution) increases significantly, but productivity (quality of effort) decreases significantly. At the same time, productivity differences across participants decrease, even reverse, to the extent that above-average performers end up below average and vice versa. The latter can be attributed to increased randomness of solution strategies. Our findings suggest that "smart drugs" increase motivation, but a reduction in quality of effort, crucial to solve complex problems, annuls this effect.

Correction to: An enhanced multi-operator differential evolution algorithm for tackling knapsack optimization problem

Correction to: An enhanced multi-operator differential evolution algorithm for tackling knapsack optimization problem

An enhanced multi-operator differential evolution algorithm for tackling knapsack optimization problem

An enhanced multi-operator differential evolution algorithm for tackling knapsack optimization problem

Block SMRT and knapsack optimization-based sequency selector for robust, imperceptible, and payload-efficient color image watermarking for binary watermark

Watermarking is a generic strategy for overcoming numerous issues associated with multimedia security. Performance of a watermarking system in terms of imperceptibility, robustness and payload is highly dependent on the positions used for embedding. The choice of sequency packets plays an important role in the performance when Sequency-based Mapped Real Transform (SMRT) is used for embedding. A novel sequency selector for robust, imperceptible and payload-efficient color image watermarking of binary watermark is proposed using SMRT and two-level optimization. SMRT of block partitioned Cb channel of cover image is input to sequency selector where results of first-level optimization, based on imperceptibility and robustness, along with payload is optimized using knapsack optimization in the second level. The binary watermark is LSB embedded in SMRT coefficients present in each packet of the optimal combination of sequencies chosen by the sequency selector. Simulation results show better performance compared to existing optimized and non-optimized embedding techniques.

A Novel Knapsack Algorithm-Based Energy Routing in a Microgrid

The increase in penetration of renewable energy sources has transformed the existing grid into a multisource and multipath energy network. For real-time energy transactions in the new microgrid, it is essential to realize an energy router interface, which is the core of the energy internet. The energy router controls the bidirectional energy and data flow and achieves end-to-end energy transmission efficiently. With this consideration, this article proposes a new energy routing algorithm based on the knapsack optimization technique. The proposed work aims to minimize the net energy from the main grid and efficiently utilize solar photovoltaic (SPV) energy through meticulous energy routing. The effectiveness of the proposed work is validated for case studies with various types of loads viz residential, non-residential, and electric vehicle loads. In this work, the best set of loads for optimal energy routing with minimum energy costs are determined. The results show a substantial reduction ranging from 16 to 28% in the peak energy drawn from the grid and at the same time, the cost of electricity to be paid to the utility is noticeably reduced in the range of 39% to 50% for various load types. Further, a sensitivity analysis is carried out to evaluate the effect of variations in input parameters such as PV output, and load demand on the cost of electricity.

Investigation of The Effect of Feeding Period in Honey Bee Algorithm

In the study, it was investigated the ejaculation ability and semen quality of drones, according to feeding with pollen in different periods. In the first step of the study, 16 %, 32 %, 47 %, 63 %, 79 %, and 100 % feeding periods were applied to the drones, for investigating the effect on ejaculation ability, and the semen quality of drones was investigated. While investigating these feeding period effects “0-1”, bonded, and unbounded knapsack optimization problems were used. After the most effective feeding period was determined, this period was applied to the traveling salesman and liquid storage tank problems in the second step of the study. In the analysis of the traveling salesman problem, it was determined the shortest way between two cities. Analysis of the liquid storage tank problem, it was determined the minimum connector areas. As a result, the analysis results showed that the performance of the artificial bee colony algorithm is very good while solving too complex engineering optimization problems.

Prioritizing municipal lead mitigation projects as a relaxed knapsack optimization: a method and case study

AbstractLead pipe remediation budgets are limited and ought to maximize public health impact. This goal implies a nontrivial optimization problem; lead service lines connect water mains to individual houses, but any realistic replacement strategy must batch replacements at a larger scale. Additionally, planners typically lack a principled method for comparing the relative public health value of potential interventions and often plan projects based on nonhealth factors. This paper describes a simple process for estimating child health impact at a parcel level by cleaning and synthesizing municipal datasets that are commonly available but seldom joined due to data quality issues. Using geocoding as the core record linkage mechanism, parcel‐level toxicity data can be combined with school enrollment records to indicate where young children and lead lines coexist. A harm metric of estimated exposure‐years is described at the parcel level, which can then be aggregated to the project level and minimized globally by posing project selection as a 0/1 knapsack problem. Simplifying for use by nonexperts, the implied linear programming relaxation is solved with the greedy algorithm; ordering projects by benefit cost ratio produces a priority list that planners can then consider holistically alongside harder to quantify factors. A case study demonstrates the successful application of this framework to a small U.S. city's existing data to prioritize federal infrastructure funding.

Hit Ratio and Content Quality Tradeoff for Adaptive Bitrate Streaming in Edge Caching Systems

This paper addresses the tradeoff problem between hit ratio and content quality in edge caching systems for multiuser adaptive bitrate streaming (ABS) services. A dynamic policy for cache decision and quality level selection for each ABS content during every cache cycle is proposed. Achieving this policy is NP-complete. For this, the considered problem is transformed into a nested multidimensional 0/1 knapsack optimization problem which is then resolved by a cooperative transfer learning-accelerated genetic algorithm. Performance evaluation demonstrates an adaptation of the proposed algorithm on various video stream popularity models in terms of algorithmic convergence and cache balancing.

A dynamic programming optimization for traffic microsimulation modelling of a mass evacuation

• A Framework for using all modes, including transit and school buses in evacuation. • Optimize auto-bus composition for an efficient evacuation. • A case of Halifax is tested with a 5–20% auto-based demand served by buses. • Results from all mode evacuation yield a vehicular traffic reduction of 3.9–7.7%. • An evacuation time reduction of 9–22.7% is achieved in all mode evacuation. This study develops a novel framework to formalize the optimal utilization of all available modes of transportation , particularly transit and school buses for a mass evacuation. The study develops an “All-Mode Evacuation Decision Support Tool (AMEDST)” to determine an optimum auto-bus composition that yields an improvement in evacuation time and network congestion. The study follows a Knapsack optimization and adopts a solution algorithm called Dynamic Programming within a Python platform to optimally allocate buses to evacuees exposed to higher level of vulnerabilities. A traffic microsimulation model follows a dynamic traffic assignment process to simulate evacuation scenarios using all available modes. Results from the traffic simulation yield a vehicular traffic reduction of 3.9–7.7% and an evacuation time reduction of 9–22.7% if 5–20% of auto-based demand are served by buses. The tool will help emergency personnel evaluate alternative scenarios for making informed decisions regarding resource allocation and emergency budget policies for large-scale evacuations.

Energy-efficient user association with load-balancing for cooperative IIoT network within B5G era

As one of the key technologies of 5G wireless communication technology, cooperative multi-access edge computing allows one device to associate multiple edge nodes simultaneously, namely multi-association, which can provide scalable communication services with characteristics of high reliability, massive connectivity and low latency for promising Industrial Internet of Things (IIoT). Effective association between edge nodes and devices is the prerequisite for providing high quality communication services in dense deployed IIoT networks. Most of state of art researches focus on the user association problem in single-association scenario. There are rarely no solutions presented for the considered user association problem with multi-association. In this paper, user association, power allocation and edge node deployment are jointly considered for load balance and energy efficiency under the multi-association mechanism. The problem is formulated as a nested knapsack optimization problem (NKOP) with energy efficiency and load balancing as objective functions and power and signal quality as constraints. Differential evolution with Monte Carlo and sequential quadratic programming (DMS) algorithm is proposed to solve this problem, which decouples the problem into three parts, user association, power allocation and optimizing the location of edge nodes. Numerical results show that: (1) Compared with the single-association, multi-association with power allocation can provide better signal quality and improve energy efficiency; (2) Proposed DMS algorithm is feasible and stable for optimal deployment of edge nodes. These works together provide good reference for edge node deployment of high-density IIoT application scenarios.

An intelligent content caching protocol for connected vehicles

AbstractIn the future smart cities, connected vehicles provide various infotainment and nonsafety services using vehicle to vehicle and vehicle to road side units (RSUs) communications. To enable these infotainment services, content providers cache their popular data on the RSU storage to make it quickly accessible for the vehicles. In this article, we propose a novel content caching protocol used by mobile network operators. The proposed protocol uses knapsack optimization algorithm to allocate the variable‐sized contents to the RSUs based on their utility. Results show that the proposed protocol provides 13% and 70% approximately higher download data than the market matching and random caching, respectively.

Designing Strong Privacy Metrics Suites Using Evolutionary Optimization

The ability to measure privacy accurately and consistently is key in the development of new privacy protections. However, recent studies have uncovered weaknesses in existing privacy metrics, as well as weaknesses caused by the use of only a single privacy metric. Metrics suites, or combinations of privacy metrics, are a promising mechanism to alleviate these weaknesses, if we can solve two open problems: which metrics should be combined and how. In this article, we tackle the first problem, i.e., the selection of metrics for strong metrics suites, by formulating it as a knapsack optimization problem with both single and multiple objectives. Because solving this problem exactly is difficult due to the large number of combinations and many qualities/objectives that need to be evaluated for each metrics suite, we apply 16 existing evolutionary and metaheuristic optimization algorithms. We solve the optimization problem for three privacy application domains: genomic privacy, graph privacy, and vehicular communications privacy. We find that the resulting metrics suites have better properties, i.e., higher monotonicity, diversity, evenness, and shared value range, than previously proposed metrics suites.

Quantum-based algorithm and circuit design for bounded Knapsack optimization problem

The Knapsack Problem is a prominent problem that is used in resource allocation and cryptography. This paper presents an oracle and a circuit design that verifies solutions to the decision problem form of the Bounded Knapsack Problem. This oracle can be used by Grover Search to solve the optimization problem form of the Bounded Knapsack Problem. This algorithm leverages the quadratic speed-up offered by Grover Search to achieve a quantum algorithm for the Knapsack Problem that shows improvement with regard to classical algorithms. The quantum circuits were designed using the Microsoft Q# Programming Language and verified on its local quantum simulator. The paper also provides analyses of the complexity and gate cost of the proposed oracle. The work in this paper is the first such proposed method for the Knapsack Optimization Problem.

Authenticated secret key generation in delay-constrained wireless systems

With the emergence of 5G low-latency applications, such as haptics and V2X, low-complexity and low-latency security mechanisms are needed. Promising lightweight mechanisms include physical unclonable functions (PUF) and secret key generation (SKG) at the physical layer, as considered in this paper. In this framework, we propose (i) a zero round trip time (0-RTT) resumption authentication protocol combining PUF and SKG processes, (ii) a novel authenticated encryption (AE) using SKG, and (iii) pipelining of the AE SKG and the encrypted data transfer in order to reduce latency. Implementing the pipelining at PHY, we investigate a parallel SKG approach for multi-carrier systems, where a subset of the subcarriers are used for SKG and the rest for data transmission. The optimal solution to this PHY resource allocation problem is identified under security, power, and delay constraints, by formulating the subcarrier scheduling as a subset-sum 0−1 knapsack optimization. A heuristic algorithm of linear complexity is proposed and shown to incur negligible loss with respect to the optimal dynamic programming solution. All of the proposed mechanisms have the potential to pave the way for a new breed of latency aware security protocols.

A co-evolutionary decomposition-based algorithm for the bi-level knapsack optimisation problem

Bi-level optimisation problems (BOPs) are a class of challenging problems with two levels of optimisation tasks. These problems allow to model a large number of real-life situations in which a first decision maker, hereafter the leader, optimises his objective by taking the follower's response to his decisions explicitly into account. In this context, a new proposed algorithm called CODBA-II was suggested to solve combinatorial BOPs. The latter was able to improve the quality of generated bi-level solutions regarding to recently proposed methods. In fact, a wide range of applications fit the bi-level programming framework and real-life implementations still scarce. For this reason, we propose in this paper a co-evolutionary decomposition-based bi-level algorithm for the bi-level knapsack optimisation problem. The computational algorithm turned out to be quite efficient on both computation time and solution quality regarding to other competitive EAs.

A co-evolutionary decomposition-based algorithm for the bi-level knapsack optimisation problem

A co-evolutionary decomposition-based algorithm for the bi-level knapsack optimisation problem

Analysis of characteristics of the local search method in the process of solving the knapsack optimization problem in the decision support systems

This scientific paper deals with the knapsack optimization task and solving method based on the local search in the boolean space. The obtained by the author analytical formulas for assessment of the upper bound for the amount of enumeration of points in the boolean space and lower bound for the quality of suboptimal solutions in the process of solving the knapsack optimization problem by the local search method are also discussed. Finally, the results of experimental research of the amount of enumeration of points in the boolean space and quality of suboptimal solutions for the different initial conditions of the knapsack optimization task, which confirmed the analytical formulas, are also given.