Squirrel search algorithm (SSA) is a new biological-inspired optimization algorithm, which has been proved to be more effective for solving unimodal, multimodal, and multidimensional optimization problems. However, similar to other swarm intelligence-based algorithms, SSA also has its own disadvantages. In order to get better global convergence ability, an improved version of SSA called ISSA is proposed in this paper. Firstly, an adaptive strategy of predator presence probability is proposed to balance the exploration and exploitation capabilities of the algorithm. Secondly, a normal cloud model is introduced to describe the randomness and fuzziness of the foraging behavior of flying squirrels. Thirdly, a selection strategy between successive positions is incorporated to preserve the best position of flying squirrel individuals. Finally, in order to enhance the local search ability of the algorithm, a dimensional search enhancement strategy is utilized. 32 benchmark functions including unimodal, multimodal, and CEC 2014 functions are used to test the global search ability of the proposed ISSA. Experimental test results indicate that ISSA provides competitive performance compared with the basic SSA and other four well-known state-of-the-art optimization algorithms.

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Lightning attachment procedure optimization (LAPO) is a new global optimization algorithm inspired by the attachment procedure of lightning in nature. However, similar to other metaheuristic algorithms, LAPO also has its own disadvantages. To obtain better global searching ability, an enhanced version of LAPO called ELAPO has been proposed in this paper. A quasi-opposition-based learning strategy is incorporated to improve both exploration and exploitation abilities by considering an estimate and its opposite simultaneously. Moreover, a dimensional search enhancement strategy is proposed to intensify the exploitation ability of the algorithm. 32 benchmark functions including unimodal, multimodal, and CEC 2014 functions are utilized to test the effectiveness of the proposed algorithm. Numerical results indicate that ELAPO can provide better or competitive performance compared with the basic LAPO and other five state-of-the-art optimization algorithms.

Conceptual comparative analysis of EBL with other metaheuristic algorithmsBroadly, all nature-inspired metaheuristics algorithms give a similar appearance superficially and are generally differentiated on the basis of their solution updating strategy (Jain et al. 2018). In the section, a brief conceptual comparative analysis of EBL is conducted in this section with respect to teaching-learning-based optimization (TLBO), lightning attachment procedure optimization (LAPO) and squirrel search algorithm (SSA).