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The proposed algorithm is tested on the Moving Peak Benchmark.
The performance of the proposed harmony search is verified by using the well-known dynamic test problem called the Moving Peak Benchmark (MPB) with a variety of peaks.
The experiments were conducted on Moving Peak Benchmark which is the most well-known benchmark in this domain and results have been compared with those of the state-of-the art methods.
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The performance of the proposed algorithm has been tested on two different instances of the Moving Peaks Benchmark (MPB) problems.
We validate performance of our method by considering both the dynamic generalized assignment problem and the moving peaks benchmark.
Experimental studies based on a set of popular algorithms show that the performance of algorithms is significantly affected by these challenging issues on the moving peaks benchmark.
The performance of the proposed approach is evaluated on Moving Peaks Benchmark (MPB) and a modified version of it, called MPB with pendulum motion (PMPB).
An extensive set of experiments is conducted on a variety of dynamic environments, generated by the moving peaks benchmark, to evaluate the performance of the proposed approach.
The algorithms have been tested on the Moving Peaks Benchmark and the dynamic version of the Ackley, Griewank and Rastrigin functions.
Extensive experiments were conducted on the Moving Peaks Benchmark (MPB) and six dynamic test functions proposed in the IEEE Congress on Evolutionary Computation (CEC 2009) were used to evaluate the performance of the proposed algorithm.
The efficiency of SAIW is validated on 22 static test problems, moving peaks benchmarks (MPB) and a real-world problem for a radar system design.
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