A single-solution–compact hybrid algorithm for continuous optimization

Abstract

This research paper proposes a memetic algorithm based on a hybridization of two metaheuristic approaches, a single-solution method and a compact optimization algorithm. The hybrid algorithm is thus a bi-module framework, where each module encapsulates a different search logic. Both modules use the Non-Uniform Mutation, although with different flavors: the first one acting on a single variable at a time, the second one acting on multiple variables. Hence, the algorithm is dubbed "compact Single/Multi Non-Uniform Mutation" (in short, cSM). It is designed for being suitable for tackling optimization problems on memory-constrained devices, i.e., devices for which the available memory may be not enough to run population-based metaheuristics. The performance of cSM is evaluated by an extensive comparative analysis including 12 state-of-the-art memory-saving (also called "lightweight") algorithms on three well-known testbeds, namely the BBOB, the CEC-2014, and CEC-2017 benchmarks, as well as seven real-world optimization problems included in the CEC-2011 benchmark. In the case of the CEC benchmarks, our method is also compared against the top (population-based) algorithms that participated in respective competitions. The numerical results indicate that, compared to all the other lightweight algorithms under study, the proposed algorithm is better at handling most functions at different dimensionalities, especially in the case of non-separable problems.

Metaheuristics in the Balance: A Survey on Memory‐Saving Approaches for Platforms with Seriously Limited Resources

“…However, it is possible to endow both cMeta and sMeta with algorithmic moves that handle multiple variables at the same time. A concrete example is that recently proposed in [44], where the cSM approach was shown to be successful, particularly in multimodal problems. (v) Among compact algorithms, some (e.g., rcGA) work well in lower dimensionalities, while others (e.g., cPSO) perform especially well in larger dimensionalities (a similar observation can be made for single-solution algorithms).…”

“…cSNUM deals well with separable problems of diferent dimensionalities. Similarly, the Single/Multi Non-Uniform Mutation (cSM) algorithm [44] is a hybrid algorithm that combines an rcGA-like structure with the nonuniform mutation (NUM) operator. Tis is very similar to cSNUM but perturbs all variables instead of just one.…”

“…Furthermore, we review and report some signifcant applications of these algorithms, giving examples to practitioners having to deal with these scenarios and facilitating the search for reactive algorithmic solutions that are already present in the literature in one document. For benchmarking and other performance-related numerical results, we refer to [42][43][44] and most of the articles included in this survey.…”

Vibrational Genetic Algorithm-Based Deployment of Wireless Sensor Networks With Heterogeneous Nodes in Irregularly Shaped Areas

“…Initially, a low mutation rate is set, and then, after each generation or iteration, the mutation rate is increased, thereby increasing the probability of gene alterations. In this way, it initially allows the algorithm to explore a broad search space with a low mutation rate and then focus on more specific search areas with a higher mutation rate, enabling access to better solutions [47].…”

Maximum Lyapunov Exponent-based Multiple Chaotic Slime Mold Algorithm for Real-world Optimization

“…In summary, we carry out a series of comparison experiments under different dimensions on the CEC2017 standard test set and analyze the experimental results using three distinct data evaluation methods [51]. The analysis shows that MCSMA is quite competitive in terms of the overall solution quality, convergence rate, performance stability and robustness of the algorithm.…”