A new bio-inspired metaheuristic algorithm for solving optimization problems based on walruses behavior

Abstract

This paper introduces a new bio-inspired metaheuristic algorithm called Walrus Optimization Algorithm (WaOA), which mimics walrus behaviors in nature. The fundamental inspirations employed in WaOA design are the process of feeding, migrating, escaping, and fighting predators. The WaOA implementation steps are mathematically modeled in three phases exploration, migration, and exploitation. Sixty-eight standard benchmark functions consisting of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, CEC 2015 test suite, and CEC 2017 test suite are employed to evaluate WaOA performance in optimization applications. The optimization results of unimodal functions indicate the exploitation ability of WaOA, the optimization results of multimodal functions indicate the exploration ability of WaOA, and the optimization results of CEC 2015 and CEC 2017 test suites indicate the high ability of WaOA in balancing exploration and exploitation during the search process. The performance of WaOA is compared with the results of ten well-known metaheuristic algorithms. The results of the simulations demonstrate that WaOA, due to its excellent ability to balance exploration and exploitation, and its capacity to deliver superior results for most of the benchmark functions, has exhibited a remarkably competitive and superior performance in contrast to other comparable algorithms. In addition, the use of WaOA in addressing four design engineering issues and twenty-two real-world optimization problems from the CEC 2011 test suite demonstrates the apparent effectiveness of WaOA in real-world applications. The MATLAB codes of WaOA are available in https://uk.mathworks.com/matlabcentral/profile/authors/13903104.

Predictive analysis of concrete slump using a stochastic search-consolidated neural network

“…Apart from data enrichment, feature selection methods (e.g., the used PCA) can be applied toward dimension reduction. Last but not least, the outstanding model of this research (i.e., the SFS-NN-MLP) can be compared to other hybrid tools with two perspectives (i) replacing the SFS with newest optimizers (e.g., walrus optimization algorithm [ 89 ], one-to-one-based optimizer [ 90 ], gold rush optimizer [ 91 ], etc. ): and (ii) replacing the NN-MLP with other predictors (e.g., ANFIS [ 92 ] and random forest [ 93 ]).…”

Swarm flip-crossover algorithm: a new swarm-based metaheuristic enriched with a crossover strategy

“…As mentioned previously, there are two sub-walks. The first sub-walk is walking toward the swarm's center, as stated in (6). The second sub-walk is walking away from the swarm's center, as stated in (7).…”

“…Many metaheuristics developed in recent years were constructed based on swarm intelligence. Many of these metaheuristics use animal behavior as metaphors, such as walrus optimization algorithm (WaOA) [6], coati optimization algorithm (COA) [7], osprey optimization algorithm (OOA) [8], slime mold algorithm (SMA) [9], komodo mlipir algorithm (KMA) [10], zebra optimization algorithm (ZOA) [11], pelican optimization algorithm (POA) [12], golden jackal optimization (GJO) [13], clouded leopard optimization (CLO) [14], Siberian tiger optimization (STO) [15], marine predator algorithm (MPA) [16], Tasmanian devil [17], northern goshawk optimization (NGO) [18], and so on. Some metaheuristics utilized their member as metaphors, such as three influential member-based optimizations (TIMBO) [19], multileader optimization (MLO) [20], mixed leader-based optimization (MLBO) [21], hybrid leader-based optimization (HLBO) [22], and so on.…”

Research on hybrid reservoir scheduling optimization based on improved walrus optimization algorithm with coupling adaptive ε constraint and multi-strategy optimization

“…Taking a typical flood process as an example, the objective function of the optimization model is designed to achieve these objectives. where: , , represent the normalized maximum storage capacities of the three reservoirs after flood scheduling, in billions of m 3 ; represents the standardized peak flow at the downstream control point, m 3 /s; ω 1 , ω 2 , ω 3 , ω 4 are the weight factors for objectives 1, 2, 3, and 4, respectively (the weight factors in this paper are referenced from the research results of Chen et al 28 ).To eliminate the influence of different units, the original values can be normalized using Eqs. ( 2 ) and ( 3 ): …”