Chaos optimization algorithm (COA) is an interesting alternative in a global optimization problem. Due to the non-repetition and ergodicity of chaos, it can explore the global search space at higher speeds than stochastic searches that depend on probabilities. To adjust the solution obtained by COA, guided local search algorithm (GLS) is integrated with COA to form a hybrid algorithm. GLS is a metaheuristic optimization algorithm that combines elements of local search with strategic guidance to efficiently explore the solution space. This study proposes a chaotic guided local search algorithm to search for global solutions. The proposed algorithm, namely COA-GLS, contributes to optimization problems by providing a balance between quick convergence and good solution quality. Its combination of local refinement, strategic guidance, diversification strategies, and adaptability makes it a powerful metaheuristic capable of efficiently navigating complex solution spaces and finding high-quality solutions in a relatively short amount of time. Simulation results show that the present algorithms significantly outperform the existing methods in terms of convergence speed, numerical stability, and a better optimal solution than other algorithms.
The cable-trench problem is defined as a linear combination of the shortest path and the minimum spanning tree problem. In particular, the goal is to find a spanning tree that simultaneously minimizes its total length and the total path length from a pre-defined root to all other vertices. Both, the minimum spanning tree and the shortest path problem are known to be efficiently solvable. However, a linear combination of these two objectives results in a highly complex problem. In this article, we introduce the bi-objective cable-trench problem which separates the two cost functions. We show that in general, the bi-objective formulation has additional compromise solutions compared to the cable-trench problem in its original formulation. To determine the set of non-dominated points and efficient solutions, we use (varepsilon )-constraint scalarizations in combination with a problem-specific cutting plane. Moreover, we present numerical results on different types of graphs analyzing the impact of density and cost structure on the cardinality of the non-dominated set and the solution time.