Combinatorial optimization by stochastic evolution

Saab, Youssef; Rao, Vasant B.

doi:10.1109/43.75636

Cited by 85 publications

(26 citation statements)

References 27 publications

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“…Although SimE was used for this paper, any stochastic optimization algorithm (e.g. Genetic Algorithm [18], Tabu Search [16,17], Simulated Annealing [34], Stochastic Evolution [37,38], Ant Colony Optimization [7,10], etc.) can be used instead.…”

Section: Fuzzy Simulated Evolution Algorithm For Dlan Topology Designmentioning

confidence: 99%

A new fuzzy operator and its application to topology design of distributed local area networks

Khan

Engelbrecht

2007

Information Sciences

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Section: Fuzzy Simulated Evolution Algorithm For Dlan Topology Designmentioning

confidence: 99%

A new fuzzy operator and its application to topology design of distributed local area networks

Khan

Engelbrecht

2007

Information Sciences

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“…These approaches tend to suffer from an excessive computational time with the increase of variables. To overcome this difficulty, modern techniques such as simulated annealing (Cerny, 1985;Kirkpatrick et al, 1983), stochastic evolution (Saab and Rao, 1991), genetic algorithms (Goldberg, 1989) and Tabu search (Rajan and Mohan, 2004) have been proposed as alternatives where the problem size precludes traditional techniques. These techniques are completely distinct from classical programming and trial-and-error heuristic methods.…”

Section: Introductionmentioning

confidence: 99%

Hybrid particle swarm optimization: Evolutionary programming approach for solving generation maintenance scheduling problem

G¹,

Christober²

2013

Sci. Res. Essays

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This paper presents a hybrid particle swarm optimization based genetic algorithm and hybrid particle swarm optimization based evolutionary programming for solving long-term generation maintenance scheduling problem. In power system, maintenance scheduling is being done upon the technical requirements of power plants and preserving the grid reliability. The objective function is to sell electricity as much as possible according to the market clearing price forecast. While in power system, technical viewpoints and system reliability are taken into consideration in maintenance scheduling with respect to the economical viewpoint. It will consider security constrained model for preventive maintenance scheduling such as generation capacity, duration of maintenance, maintenance continuity, spinning reserve and reliability index are being taken into account. The proposed hybrid methods are applied to an IEEE test system that consist 24 buses with 32 generating unit system.

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“…If R is too small, the algorithm will not have enough time to improve the initial solution, and if R is too large, the algorithm may waste too much time during the later generations. Experimental studies indicate that a value of R between 10 and 20 gives good results [6]. Finally, the variable ρ is a counter used to decide when to stop the search.…”

Section: Stochastic Evolution (Stoce)mentioning

confidence: 99%

“…Stochastic evolution (StocE) [6] and Simulated Evolution (SimE) [5] are evolutionary iterative search algorithms, similar to other well known iterative non-deterministic heuristics such as Simulated Annealing (SA), Genetic Algorithms (GA) and Tabu Search (TS) [8]. The two algorithms are inspired by the alleged behavior of biological processes, however, they differ fundamentally in how the principles of evolution are applied.…”

Section: Introductionmentioning

confidence: 99%