Name
Abstract | ||
|---|---|---|
Evolutionary Algorithms are amongst the best known methods of solving difficult constraint optimization problems, for which traditional methods are not applicable. However, there are no inbuilt or organic mechanisms available in Evolutionary Algorithms for handling constraints in optimization problems. These problems are solved by converting or treating them as unconstrained optimization problems. Several constraint handling techniques have been developed and reported in literature, of which, the penalty factor and feasibility rules are the most promising and widely used for such purposes. However, each of these techniques has its own advantages and disadvantages and often require fine tuning of one or more parameters, which in itself becomes an optimization problem. This paper presents a hybrid constraint handling technique for a two population adaptive coevolutionary algorithm, which uses a self determining and regulating penalty factor method as well as feasibility rules for handling constraints. Thus, the method overcomes the drawbacks in both the methods and utilizes their strengths to effectively and efficiently handle constraints. The simulation on ten benchmark problems demonstrates the efficacy of the approach. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CEC.2009.4983265 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
penalty factor,constraint handling technique,known method,optimization problem,evolutionary optimization,penalty factor method,hybrid constraint handling technique,feasibility rule,novel hybrid constraint handling,unconstrained optimization problem,difficult constraint optimization problem,evolutionary algorithms,stochastic processes,pediatrics,optimization,strontium,algorithm design and analysis,evolutionary algorithm,tuning,constraint optimization,evolutionary computation | Population,Mathematical optimization,Algorithm design,Evolutionary algorithm,Computer science,Fine-tuning,Evolutionary computation,Artificial intelligence,Optimization problem,Machine learning,Constrained optimization,Penalty method | Conference |
Citations | PageRank | References |
7 | 0.59 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ashish Mani | 1 | 23 | 6.88 |
| C. Patvardhan | 2 | 78 | 12.28 |