Multi objective barnacle mating optimization for control design of a pendulum system
This paper presents a MultiObjective Barnacle Mating Optimization (MOBMO) and its application to optimize controller parameters for an inverted pendulum system. The algorithm is an extended version of a single-objective Barnacle Mating Optimization (BMO). In terms of solving a complex problem that h...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2020
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/35295/ http://umpir.ump.edu.my/id/eprint/35295/1/Multi%20objective%20barnacle%20mating%20optimization%20for%20control%20design%20of%20a%20pendulum%20system.pdf http://umpir.ump.edu.my/id/eprint/35295/2/Multi%20objective%20barnacle%20mating%20optimization%20for%20control%20design%20of%20a%20pendulum%20system_FULL.pdf |
| Summary: | This paper presents a MultiObjective Barnacle Mating Optimization (MOBMO) and its application to optimize controller parameters for an inverted pendulum system. The algorithm is an extended version of a single-objective Barnacle Mating Optimization (BMO). In terms of solving a complex problem that has two conflicting objectives, a multiobjective type BMO is needed. Therefore, in the proposed MOBMO, nondominated sorting and crowding distance approaches are incorporated into BMO as a technique to formulate the multiobjective algorithm. The proposed algorithm is tested on various multiobjective benchmark functions. Its performance in terms of accuracy and diversity attainment to find a theoretical pareto front solution is analyzed. Moreover the proposed MOBMO is applied to optimize control parameters for PD controls of a pendulum system. The performance of the proposed MOBMO is compared with Multiobjective Water Cycle Algorithm (MOWCA). Result of the benchmark functions test shows that the proposed algorithm has attained a higher accuracy and a competitive diversity in locating the theoretical front solution. For its application to optimize PD control parameters, both MOWCA and MOBMO have successfully attained a good pareto front solution and controlled the pendulum sufficiently good. Overall performance, the proposed MOBMO has outperformed MOWCA for accuracy attainment and achieved the same level of diversity performance. |
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