Algorithms for solving large-scale 0–1 goal programming and its application to reliability optimization problem

Gen, Mitsuo; Sato, Masato; Ida, Kenichi; Lee, J. U.

doi:10.1016/0360-8352(89)90117-4

Cited by 16 publications

(5 citation statements)

References 14 publications

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“…A variety of algorithms, as summarized in Tillman et al (1977), and more recently by Kuo & Prasad (2000), Kuo & Wan (2007), including exact methods, heuristics and meta-heuristics have already been proposed for the RRAP. An exact optimal solution is obtained by exact methods such as cutting plane method (Tillman, 1969), branch-and-bound algorithm (Chern & Jan, 1986;Ghare & Taylor, 1969), dynamic programming (Bellman & Dreyfus, 1958;Fyffe et al, 1968;Nakagawa & Miyazaki, 1981;Yalaoui et al, 2005), and goal programming (Gen et al, 1989). However, as the size of problem gets larger, such methods are difficult to apply to get a solution and require more computational effort.…”

Section: The Reliability-redundancy Allocation Problem (Rrap)mentioning

confidence: 99%

Real-World Applications of Genetic Algorithms

Roeva¹

2012

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Dr. Olympia Roeva received the M.Sc. degree (1998) and Ph.D. degree (2007) from the Technical University -Sofia, Bulgaria. At present she is an associate professor in Department of Bioinformatics and Mathematical Modeling, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences. Dr. Roeva is one of the scientists playing a significant role in the renovation of International Journal Bioautomation. She has received various scientific awards, including a Diploma from Union of Scientists in Bulgaria for High Scientific Achievements 2008. Her research interests are in the fields of modeling, optimization and control of cultivation processes, functional state modeling approach, metaheuristic algorithms and generalized nets.

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Section: The Reliability-redundancy Allocation Problem (Rrap)mentioning

confidence: 99%

Real-World Applications of Genetic Algorithms

Roeva¹

2012

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“…This problem had been a benchmark problem for various researchers (Gen, 1975;Gen et al, 1989). The problem is to maximize the system reliability that is subjected to three nonlinear constraints with parallel redundant units in the subsystem.…”

Section: Test Problem Related To Type D Formulationmentioning

confidence: 99%

Improved and generalized learning strategies for dynamically fast and statistically robust evolutionary algorithms

Dashora

Kumar

Shukla³

et al. 2008

Engineering Applications of Artificial Intelligence

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This paper characterizes general optimization problems into four categories based on the solution representation schemes, as they have been the key to the design of various evolutionary algorithms (EAs). Four EAs have been designed for different formulations with the aim of utilizing similar and generalized strategies for all of them. Several modifications to the existing EAs have been proposed and studied. First, a new tradeoff function-based mutation has been proposed that takes advantages of Cauchy, Gaussian, random as well as chaotic mutations. In addition, a generalized learning rule has also been proposed to ensure more thorough and explorative search. A theoretical analysis has been performed to establish the convergence of the learning rule. A theoretical study has also been performed in order to investigate the various aspects of the search strategy employed by the new tradeoff-based mutations. A more logical parameter tuning has been done by introducing the concept of orthogonal arrays in the EA experimentation. The use of noise-based tuning ensures the robust parameter tuning that enables the EAs to perform remarkably well in the further experimentations. The performance of the proposed EAs has been analyzed for different problems of varying complexities. The results prove the supremacy of the proposed EAs over other well-established strategies given in the literature. r

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“…This application involved 99 subsystems with 10 constraints each. Additional examples with integer programming were studied by [15][16][17]. Linear programming was used to generate solutions for redundancy allocation problems [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Reliability–Redundancy Allocation Problems: A Review of the Evolutionary Algorithms

Zaka¹,

Jabeen²,

Khan³

2022

Computers, Materials &Amp; Continua

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The study of optimization methods for reliability-redundancy allocation problems is a constantly changing field. New algorithms are continually being designed on the basis of observations of nature, wildlife, and humanity. In this paper, we review eight major evolutionary algorithms that emulate the behavior of civilization, ants, bees, fishes, and birds (i.e., genetic algorithms, bee colony optimization, simulated annealing, particle swarm optimization, biogeography-based optimization, artificial immune system optimization, cuckoo algorithm and imperialist competitive algorithm). We evaluate the mathematical formulations and pseudo-codes of each algorithm and discuss how these apply to reliability-redundancy allocation problems. Results from a literature survey show the best results found for series, series-parallel, bridge, and applied case problems (e.g., overspeeding gas turbine benchmark). Review of literature from recent years indicates an extensive improvement in the algorithm reliability performance. However, this improvement has been difficult to achieve for high-reliability applications. Insights and future challenges in reliability-redundancy allocation problems optimization are also discussed in this paper.

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Algorithms for solving large-scale 0–1 goal programming and its application to reliability optimization problem

Cited by 16 publications

References 14 publications

Real-World Applications of Genetic Algorithms

Real-World Applications of Genetic Algorithms

Improved and generalized learning strategies for dynamically fast and statistically robust evolutionary algorithms

Optimization of Reliability–Redundancy Allocation Problems: A Review of the Evolutionary Algorithms

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