Effect of negative campaign strategy of election algorithm in solving optimization problem
Election algorithm (EA) is an optimization technique based on minimization and coalition operations to solve competition among neurons. The Election algorithm gives the best individual of the population by enhancing both minimization and coalition operations while local search gives the best loca...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2020
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| Online Access: | http://journalarticle.ukm.my/16068/ http://journalarticle.ukm.my/16068/1/jqma-16-2-paper4.pdf |
| Summary: | Election algorithm (EA) is an optimization technique based on minimization and coalition
operations to solve competition among neurons. The Election algorithm gives the best individual
of the population by enhancing both minimization and coalition operations while local search
gives the best local solutions by testing all neighbouring solutions. Negative campaign
mechanism is one of the most important mechanism in EA for its impact on the diversification
and overcoming premature convergence of the entire search space towards optimal searching.
The challenging task lies in selecting the appropriate negative campaigning operator that leads
to optimal searching in a reasonable amount of time. The decision then becomes more difficult
and needs more trial and error to find the best negative campaigning operator. This paper
investigates the effect of negative campaign operators in enhancing the performance of EA
based on the Travelling Salesman Problem (TSP). New negative campaign operator has been
proposed based on selecting the best voter to be replaced. Experiments were conducted on the
TSP to evaluate the proposed methods. The proposed mechanism was compared with other
negative campaign operators. The result reveals the significant enhancement of the EA
performance based on the proposed method in TSP problem. |
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