A parallel genetic algorithm to solve the set-covering problem

Solar, Mauricio; Parada, Víctor; Urrutia, Rodrigo

doi:10.1016/s0305-0548(01)00026-0

Cited by 57 publications

(24 citation statements)

References 19 publications

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“…In terms of the Mean Space Covered for the mentioned DAGs with 8 processors (P = 8) (Figure 6)‚ the PGAs decrease their exploration in the measure that the degree of parallelism increases confirming the results shown in [13]. For their part the PTS show a similar performance‚ although for Mean Space Covered always less‚ and a slower decrease in the measure that the degree of parallelism increases.…”

Section: 1supporting

confidence: 74%

“…This parallel model uses K processes executed independently, and with independent information. The best result is communicated to a master process which determines the best solution and communicates this to the concurrent processes [13].…”

Section: Synchronous Network Concurrency Modelmentioning

confidence: 99%

“…The population is divided into equal parts for each GA. When one generation is finished, the GAs send their best individual to the master GA which determines the best of these sending it on to all the GAs according to the migration policy best individual over random individual (The best individual is copied over any solution for every GA [13]). Synchronous Network Concurrency Model applied to Parallel Genetic Algorithms…”

Section: Parallel Genetic Algorithm (Pga)mentioning

confidence: 99%

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Using Genetic Algorithms and Tabu Search Parallel Models to Solve the Scheduling Problem

Pinacho

Solar

Inostroza

et al. 2004

IFIP International Federation for Information Processing

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This work presents a comparison between a Parallel Genetic Algorithm (PGA) and a Parallel Tabu Search (PTS) algorithm. Both are used for solving a scheduling problem on 45 tests based on the same parallel model, (Synchronous Network Concurrency Model) and testing sequential algorithms with 2, 4 and 8 processors. The results show that the PTS algorithm obtains better results, and covers a smaller portion of the solution space.

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Section: 1supporting

confidence: 74%

Section: Synchronous Network Concurrency Modelmentioning

confidence: 99%

Section: Parallel Genetic Algorithm (Pga)mentioning

confidence: 99%

See 1 more Smart Citation

Using Genetic Algorithms and Tabu Search Parallel Models to Solve the Scheduling Problem

Pinacho

Solar

Inostroza

et al. 2004

IFIP International Federation for Information Processing

Self Cite

View full text Add to dashboard Cite

show abstract

“…The initial D s and the cooling ratio µ were set to 0.6 and 0.99. The results were shown in Table 7 where the results produced by the parallel GA (PGA) (Solar et al 2002) and the standard GA (SGA) (Caldas and Norford 2002) were also listed for comparison. In the table, P s is the population size, the "error%" column corresponds to the average percentage error of (8) where Solution(i) is the solution found in the i# run, Optimal is the optimal solution (shown in Table 6) of problem.…”

Section: Simulation Results On the Scpmentioning

confidence: 99%

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem

Wang¹,

Okazaki²

2006

Soft Comput

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The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Because there are no crossover rate and mutation rate to be selected, the proposed improved GA can be more easily applied to a problem than the conventional genetic algorithms. The proposed improved genetic algorithm is applied to solve the set-covering problem. Experimental studies show that the improved GA produces better results over the conventional one and other methods.

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“…In this approach actual solutions are found by an external decoder function, results can be further improved by adding another indirect optimization layer and then optimized by another hillclimbing algorithm. A parallel genetic algorithm (PGA) model to solve the set-covering problem is presented in Solar et al (2002). An experimental study obtained with a binary representation of the SCP, shows that PGA performs better than the sequential model in terms of the number of generations (computational time) needed to achieve solutions.…”

Section: Related Workmentioning

confidence: 99%

A new approach for solving set covering problem using jumping particle swarm optimization method

Balaji

Revathi

2015

Nat Comput

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The set covering problem (SCP) is a well known classic combinatorial NP-hard problem, having practical application in many fields. To optimize the objective function of the SCP, many heuristic, meta heuristic, greedy and approximation approaches have been proposed in the recent years. In the development of swarm intelligence, the particle swarm optimization is a nature inspired optimization technique for continuous problems and for discrete problems we have the well known discrete particle swarm optimization (DPSO) method. Aiming towards the best solution for discrete problems, we have the recent method called jumping particle swarm optimization (JPSO). In this DPSO the improved solution is based on the particles attraction caused by attractor. In this paper, a new approach based on JPSO is proposed to solve the SCP. The proposed approach works in three phases: for selecting attractor, refining the feasible solution given by the attractor in order to reach the optimality and for removing redundancy in the solution. The proposed approach has been tested on the benchmark instances of SCP and compared with best known methods. Computational results show that it produces high quality solution in very short running times when compared to other algorithms.

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A parallel genetic algorithm to solve the set-covering problem

Cited by 57 publications

References 19 publications

Using Genetic Algorithms and Tabu Search Parallel Models to Solve the Scheduling Problem

Using Genetic Algorithms and Tabu Search Parallel Models to Solve the Scheduling Problem

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem

A new approach for solving set covering problem using jumping particle swarm optimization method

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