Akademik Veri Yönetim Sistemi
EUROASIA JOURNAL OF MATHEMATICS-ENGINEERING NATURAL & MEDICAL SCIENCES, cilt.2, sa.7, ss.60-68, 2019 (Hakemli Dergi)
The difficulty in solving engineering problems creates difficulties in the selection of the methods to be
used. Nature-inspired herd intelligence-based meta-heuristic optimization techniques have recently
become the most popular algorithms for solving such problems. In this work, a new hybrid algorithm
model has been developed to adapt to various problems. The developed models were adapted to 23
Benchmark test problems in the literature and compared with meta-heuristic algorithms. The
algorithms aim to balance the optimization processes of exploration and exploitation. In the
development of a meta-heuristic algorithm, it is very difficult to achieve a balance due to its stochastic
structure. In this study, the new hybrid model improved by Multi-Verse Optimization (MVO) on the
Sine Cosine Whale Optimization Algorithm (SCWOA) hybrid model, which is available in the
literature, has increased the success of test problems. Although the SCWOA hybrid balances
exploitation and exploration, the MVSCWOA (Multi-Verse Sine Cosine Whale Optimization
Algorithm) hybrid algorithm, which was modified by modifying MVO's wormhole existence
probability (WEP) and traveling distance rate (TDR), has succeeded in improving this balance further.
WEP is used instead of 𝒓𝟏 parameter, which determines the update direction in SCA, and TDR is used
in place of 𝒂𝟐 (varies between -1 and -2) used in the update of l, which is the inter-element
multiplication parameter in WOA. The results obtained from the newly developed hybrid model have
shown that it makes the search and exploitation feature more effective by showing better results than
SCWOA, WOA, SCA, and MVO. MVSCWOA was successful in test problems.
Keywords: Benchmark, Sine Cosine Algorithm, Whale Optimization Algorithm, Multi-Verse
Optimization