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Stable matching-based selection models the selection process in MOEA/D as a stable marriage problem. By finding a stable matching between the sub problems and solutions, the solutions are assigned to sub problems to balance the convergence and the diversity. In this paper, a two-level stable matching-based selection is proposed to further guarantee the diversity of the population. More specifically,...
In this paper, we propose hybrid algorithm, 2-opt optimal (2-opt) heuristic mutation with nearest neighbor (NN) tour construction, for solving traveling salesman problem (TSP). In this method, we first initialize suboptimal solution with the help of NN tour construction then DPX crossover is being used and after that 2-opt heuristic method is applied to refine solution for global optimality. Standard...
A multi-colony ant colony optimization (ACO) algorithm consists of several colonies of ants. Each colony uses a separate pheromone table in an attempt to maximize the search area explored. Over the years, multi-colony ACO algorithms have been successfully applied on different optimization problems with stationary environments. In this paper, we investigate their performance in dynamic environments...
The performance of ant colony optimization (ACO) algorithms in tackling optimization problems strongly depends on different parameters. One of the most important parameters in ACO algorithms when addressing dynamic optimization problems (DOPs) is the pheromone evaporation rate. The role of pheromone evaporation in DOPs is to improve the adaptation capabilities of the algorithm. When a dynamic change...
An ecosystem inspired algorithm that aims to take advantage of highly distributed computer architectures is proposed. The motivation behind this work is to grasp the phenomenal properties of ecosystems and use them for large-scale real-world problems. Just as an ecosystem comprises many separate components that adapt together to form a single synergistic whole, the Artificial Ecosystem Algorithm (AEA)...
Traveling Salesman Problem (TSP) is a well-known NP-hard problem. Many algorithms were developed to solve this problem and gave the nearly optimal solutions within reasonable time. This paper presents a survey about the combination Genetic Algorithm (GA) with Dynamic Programming (DP) for solving TSP. We also setup a combination between GA and DP for this problem and experimented on 7 Euclidean instances...
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