Geodesy

Metaheuristic

Cell biology

Algorithm

Benchmark (surveying)

Engineering

Biology

Convergence (economics)

Local search (optimization)

Economics

Computer science

Mathematical optimization

Search-based software engineering

Engineering design process

Mechanical engineering

Software

Slime mold

Mathematics

Geography

Software design

Global optimization

Search algorithm

Programming language

Economic growth

Software development

Computer Methods in Applied Mechanics and Engineering

In the application of metaheuristic algorithms (MAs) to complicated optimization problem solving, it is significant to balance the exploitation and exploration to obtain a good near-optimum solution to the problem. Therefore, in this study, to balance the exploitative and explorative features of conventional MAs, a multi-strategy improved slime mould algorithm called MSMA is introduced. In MSMA, a new search equation is developed to achieve a tradeoff between exploitation and exploration. Then the dynamic random search technique is utilized as a local search engine to enhance the search efficiency of the algorithm. Finally, the adaptive mutation probability is designed to avoid premature convergence. MSMA is evaluated using 28 benchmark functions and several practical engineering issues such as welded beam design, pressure vessel design, tension/compression spring design, and UAV path planning. The simulation results based on 30 independent runs demonstrate that it is more efficient and robust than other state-of-the-art techniques from the literature according to the selected performance metrics such as mean values and standard deviations. The source code of MSMA is publicly available at https://github.com/denglingyun123/Multi-strategy-improved-slime-mould-algorithm.

A multi-strategy improved slime mould algorithm for global optimization and engineering design problems