###### Abstract  
This paper examines strategies aimed at improving search procedures in multimodal, low-dimensional domains. Here, _low-dimensional domains_ refers to a maximum of five dimensions. The present analysis assembles strategies to form an algorithm named S-EPSO, which, at its core, locates and maintains multiple optima without relying on external niching parameters, instead adapting this functionality internally. The first proposed strategy assigns socio-emotional personalities to the particles forming the swarm. The analysis also introduces a technique to help them visit secluded zones. It allocates the particles of the initial distribution to subdomains based on biased decisions. The biases reflect the subdomain's potential to contain optima. This potential is established from a balanced combination of the jaggedness and the mean-average interval descriptors developed in the study. The study compares the performance of S-EPSO to that of state-of-the-art algorithms over seventeen functions of the CEC benchmark, and S-EPSO is revealed to be highly competitive. It outperformed the reference algorithms 14 times, whereas the best of the latter outperformed the other two 10 times out of 30 relevant evaluations. S-EPSO performed best with the most challenging 5D functions of the benchmark. These results clearly illustrate the potential of S-EPSO when it comes to dealing with practical engineering optimization problems limited to five dimensions.  
s 2025 2029 May 2025  
a variety of typical mechanical systems and problems. These include the design of gearboxes [5; 6], springs, and welded joints [7], among others, and aim to optimize grinding operations to minimize production costs and maximize surface quality [7]. More specifically, Ref. [7] proposes an algorithm targeting the optimization of real-life applications. These recent papers also set out to identify optimal parameter settings in laser cutting operations [8] and to define optimal gear-shifting strategies minimizing fuel consumption in internal combustion engine vehicles [9]. Most importantly, regardless of their complexity, all these optimization studies involve not more than five variables.  
The number of variables has an even greater impact in optimization jobs involving contact simulations, such as the optimization of gear tooth profiles or bearing roller profiles, since they can quickly become very computation-intensive [10]. Optimization problems involving such mechanical elements are often limited to fewer than five variables.  
Since the current literature indicates that they define a significant proportion of the everyday optimization jobs in engineering, and because improving the dedicated optimization methods remains an open research challenge, this article focuses on configurations of this type, which may be designated as multimodal problems of relatively low dimensionality. The work develops and analyses some strategies designed to improve the performance of search procedures over multimodal landscapes comprising multiple optima. While all the strategies considered can be individually adapted and integrated within any existing algorithm, the exercise of assembling them results in an optimization algorithm that is highly competitive.