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Multi-level cross entropy optimizer (MCEO): an evolutionary optimization algorithm for engineering problems
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DOI：10.1007/s00366-017-0569-z.png)
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Abstract
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This work proposes a new meta-heuristic optimization algorithm called multi-level cross entropy Optimizer (MCEO). This algorithm is conducted by combination of a group of cross entropy operators. Situations, with a low probability for optimal point are searched with high speed, and also, locations with a high probability for existence of optimal point are investigated with a low speed and high accuracy. The algorithm is then benchmarked on 13 well-known test functions in high dimension spaces (100 dimensions), and the answers are verified by a comparative study with thermal exchange optimization, selfish herds optimization, water evaporation optimization, Moth-Flame optimization, Flower Pollination Algorithm, states of matter search, and gray wolf optimizer. The results indicate that the MCEO algorithm can provide very competitive results in comparison to these well-known meta-heuristics in a similar condition (in term of NFEs). The paper also considers solving three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) and presents a genuine application of the proposed method to the field of dam engineering. The results of the classical engineering design problems and the real application validate that the proposed algorithm is applicable to challenging difficulties with unknown search spaces.
Keywords:
Optimization
Meta-heuristic
Cross-entropy
Evolution
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