Dynamic multiobjective optimization of war resource allocation using adaptive genetic algorithms

Palaniappan, Sucheendra K.; Zein-Sabatto, Saleh; Sekmen, Ali

doi:10.1109/secon.2001.923107

Cited by 23 publications

(9 citation statements)

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“…Despite initial resistance by the research community who considered the theory of fuzzy logic controversial, the logic has received tremendous attention in the last four decades, and its applications have increased significantly. Till date, fuzzy logic has been applied to a diverse areas, from control theories to artificial intelligence [23], with applications such as analog circuit design [24], war resource allocation [25], direct current electromagnet design [26], and facility location selection [27], among many others.…”

Section: Fuzzy Logic and Multi-criteria Decision Makingmentioning

confidence: 99%

A STRIDE Model based Threat Modelling using Unified and-Or Fuzzy Operator for Computer Network Securit

Khan¹

2017

IJCNT

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Abstract:In the present era, security has become a fundamental issue in efficient and proper functioning of computer and network systems. To prevent and mitigate a system, an important issue to understand how different threats could damage a network system. Keeping this issue under consideration, this paper proposes risk assessment and modeling of threats which shows the level of any attack. STRIDE (Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, Elevation of privileges) is a model which covers numerous existing threats that are related to all security properties necessary for a secure network. An strategy has been proposed which takes the number and types of attacks as input and applies a fuzzy logic based threat assessment approach to assess the level of attack. The presented work uses a fuzzy operator, namely, unified AND-OR (UAO operator), and a decision-making approach based on a fuzzy rule.

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Section: Fuzzy Logic and Multi-criteria Decision Makingmentioning

confidence: 99%

A STRIDE Model based Threat Modelling using Unified and-Or Fuzzy Operator for Computer Network Securit

Khan¹

2017

IJCNT

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“…Numerous real-world DMOOP application areas exist, of which some are hydrothermal power scheduling [Deb et al 2007], machining of gradient material [Roy and Mehnen 2008], controller design for a time-varying unstable plant [Farina et al 2004;Huang et al 2011], war resource allocation [Palaniappan et al 2001], route optimisation according to real-time traffic [Wahle et al 2001], design optimisation of wind turbine structures [Maalawi 2011], supply chain networks [Chen and Lee 2004;Selim et al 2008], and energy-efficient routing optimisation in mobile ad hoc networks [Constantinou 2011].…”

Section: Real-world Dmoo Problemsmentioning

confidence: 99%

Benchmarks for dynamic multi-objective optimisation algorithms

Helbig

Engelbrecht

2014

ACM Comput. Surv.

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Algorithms that solve Dynamic Multi-Objective Optimisation Problems (DMOOPs) should be tested on benchmark functions to determine whether the algorithm can overcome specific difficulties that can occur in real-world problems. However, for Dynamic Multi-Objective Optimisation (DMOO), no standard benchmark functions are used. A number of DMOOPs have been proposed in recent years. However, no comprehensive overview of DMOOPs exist in the literature. Therefore, choosing which benchmark functions to use is not a trivial task. This article seeks to address this gap in the DMOO literature by providing a comprehensive overview of proposed DMOOPs, and proposing characteristics that an ideal DMOO benchmark function suite should exhibit. In addition, DMOOPs are proposed for each characteristic. Shortcomings of current DMOOPs that do not address certain characteristics of an ideal benchmark suite are highlighted. These identified shortcomings are addressed by proposing new DMOO benchmark functions with complicated Pareto-Optimal Sets (POSs), and approaches to develop DMOOPs with either an isolated or deceptive Pareto-Optimal Front (POF). In addition, DMOO application areas and real-world DMOOPs are discussed.

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“…It is noteworthy that the research in the area of Genetic Algorithms [29][30][31] has identified that, although approaches based on heuristics like PSO require less configuration parameters, they may not lead to the optimal solution. However, it is also noteworthy that, for an online decision on optimal resource scheduling, the performance analysis of GA-based approaches needs further research.…”

Section: Artificial Intelligence (Ai) Based Approachesmentioning

confidence: 99%

A Framework for Resource Allocation Strategies in Cloud Computing Environment

Arfeen

Pawlikowski

Willig

2011

2011 IEEE 35th Annual Computer Software and Applications Conference Workshops

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Abstract-This paper presents a critical evaluation of current resource allocation strategies and their possible applicability in Cloud Computing Environment which is expected to gain a prominent profile in the Future Internet. This research attempts to focus towards network awareness and consistent optimization of resource allocation strategies and identifies the issues which need further investigation by the research community. A framework for resource allocation in Cloud Computing, based on tailored active measurements, has also been proposed. The main conclusion is that network topology, traffic considerations, changing optimality criteria along with dynamic user requirements will play a dominant role in determining future Internet application architectures and protocols, shaping resource allocation strategies in Cloud Computing, for example.

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Dynamic multiobjective optimization of war resource allocation using adaptive genetic algorithms

Cited by 23 publications

References 1 publication

A STRIDE Model based Threat Modelling using Unified and-Or Fuzzy Operator for Computer Network Securit

A STRIDE Model based Threat Modelling using Unified and-Or Fuzzy Operator for Computer Network Securit

Benchmarks for dynamic multi-objective optimisation algorithms

A Framework for Resource Allocation Strategies in Cloud Computing Environment

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