6 research outputs found
A self-adaptive discrete PSO algorithm with Heterogeneous parameter
This paper presents a discrete particle swarm optimization (DPSO) algorithm with heterogeneous (non-uniform) parameter values for solving the dynamic traveling salesman problem (DTSP). The DTSP can be modeled as a sequence of static sub-problems, each of which is an instance of the TSP. In the proposed DPSO algorithm, the information gathered while solving a sub-problem is retained in the form of a pheromone matrix and used by the algorithm while solving the next sub-problem. We present a method for automatically setting the values of the key DPSO parameters (except for the parameters directly related to the computation time and size of a problem).We show that the diversity of parameters values has a positive effect on the quality of the generated results. Furthermore, the population in the proposed algorithm has a higher level of entropy. We compare the performance of the proposed heterogeneous DPSO with two ant colony optimization (ACO) algorithms. The proposed algorithm outperforms the base DPSO and is competitive with the ACO
Adjustability of a discrete particle swarm optimization for the dynamic TSP
This paper presents a detailed study of the discrete particle swarm optimization algorithm (DPSO) applied to solve the dynamic traveling salesman problem which has many practical applications in planning, logistics and chip manufacturing. The dynamic version is especially important in practical applications in which new circumstances, e.g., a traffic jam or a machine failure, could force changes to the problem specification. The DPSO algorithm was enriched with a pheromone memory which is used to guide the search process similarly to the ant colony optimization algorithm. The paper extends our previous work on the DPSO algorithm in various ways. Firstly, the performance of the algorithm is thoroughly tested on a set of newly generated DTSP instances which differ in the number and the size of the changes. Secondly, the impact of the pheromone memory on the convergence of the DPSO is investigated and compared with the version without a pheromone memory. Moreover, the results are compared with two ant colony optimization algorithms, namely the (Formula presented.)–(Formula presented.) ant system (MMAS) and the population-based ant colony optimization (PACO). The results show that the DPSO is able to find high-quality solutions to the DTSP and its performance is competitive with the performance of the MMAS and the PACO algorithms. Moreover, the pheromone memory has a positive impact on the convergence of the algorithm, especially in the face of dynamic changes to the problem’s definition
Adaptacyjny algorytm optymalizacji stadnej cząsteczek dla dynamicznego problemu komiwojażera
The main assumption of the dissertation is the application of pheromone memory in the discrete version
of the PSO algorithm (Discrete Particle Swarm Optimization) with a view to adjusting it to solving
the DTSP - DTSP (Dynamic Traveling Salesman Problem). The Traveling Salesman Problem has
not only a theoretical (many combinatorial problems can be reduced to the TSP problem), but also a
practical meaning - especially in transport, from which it originated. The reasons behind the creation
of the dynamic version of the problem are practical. What can happen very often in the road is traffic
congestion, as a result of which the route is longer. The distance between vertices may refer not only
to the distance but also, e.g. time or also incurred cost. Owing to that the scope of applications of the
static and dynamic TSP is significantly wider. In this dissertation the Dynamic Traveling Salesman
Problem was defined as a sequence of consecutive static Traveling Salesman Problems (sub-problems).
The difference between one another lies in some percent of changes in the distance matrix. Despite the
substantial number of works dedicated to both the static and dynamic problem, many questions still
remain unanswered. These especially concern the dynamic version of TSP. The following two areas are
explored in this dissertation:
• theoretical and practical analysis of the Traveling Salesman Problem, as well as its dynamic
version,
• overview of literature connected with the computational intelligence and the most important
concepts related to this field of science, among others synergy or cooperation,
• description of selected computational intelligence algorithms together with explanation of how
they work.
The subsequent chapters include:
• description of how the version of the Particle Swarm Optimization Algorithm with pheromone
suggested in the dissertation works, as well as the means of adjusting it to the discussed problem,
• analysis of the influence of the values of parameters of the prepared solutions on the quality of
the achieved results,
• description of the self-adaptive (heterogenic) version of the DPSO algorithm,
• assessment of the usefulness of knowledge regarding the solution to the previous sub-problem,
in order to accelerate the convergence of the DPSO algorithm for the new sub-problem in the
solved DTSP problem,
• comparison, by the means of static tests, of the hybrid DPSO algorithm with pheromone with
the selected computational intelligence algorithms: ACO (Ant Colony Optimization) and PACO
(Population Ant Colony Optimization).
The tool suggested in the dissertation makes use of a limited list of neighborhoods for every vertex.
This procedure reduces the overview of solution space and hence improves the rate of algorithm convergence.
It has some disadvantages, e.g. the probability of finding the solution decreases in case of a lack of an edge that would belong to the optimum solution in the vertex neighborhood. Very effective
Helsgaun's neighborhood was applied in the dissertation.
Substantial attention was also given to examination of the influence of various parameter values
on the functioning of the DPSO algorithm with pheromone, which was the purpose of creating a
heterogeneous algorithm. In algorithm every particle can have different parameter values. However,
their complete randomness may lead to chaotic solution space searching. Therefore, in order to prevent
that a proper distribution of similarities of selection of given parameter values was chosen. It was
preceded by an analysis of characteristic values of the DPSO algorithm with pheromone. The diversity
of parameter values improved the quality of the obtained results. However, the main reason behind
creating the heterogeneous version was the reduction of the number of algorithm parameters. The final
parameters were restricted to the number of iterations, size of swarm and size of neighborhood. These
are parameters, the values of which should be defined on the basis of the size of the (n) problem and
the available computational budget, since the first two parameters influence the computational time.
The third parameter influences the degree of exploration and exploitation of the solution space.
The thesis of the dissertation „The Application of Pheromone Memory and Heterogeneity in the
Discrete PSO Algorithm for the Dynamic Traveling Salesman Problem” Makes it Possible to Improve
the Quality of the Obtained Results was proved on the basis of the results of computational experiments
subjected to statistical analysis
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Ant Colony Optimization With Local Search for Dynamic Traveling Salesman Problems
For a dynamic traveling salesman problem (DTSP), the weights (or traveling times) between two cities (or nodes) may be subject to changes. Ant colony optimization (ACO) algorithms have proved to be powerful methods to tackle such problems due to their adaptation capabilities. It has been shown that the integration of local search operators can significantly improve the performance of ACO. In this paper, a memetic ACO algorithm, where a local search operator (called unstring and string) is integrated into ACO, is proposed to address DTSPs. The best solution from ACO is passed to the local search operator, which removes and inserts cities in such a way that improves the solution quality. The proposed memetic ACO algorithm is designed to address both symmetric and asymmetric DTSPs. The experimental results show the efficiency of the proposed memetic algorithm for addressing DTSPs in comparison with other state-of-the-art algorithms
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A survey of swarm intelligence for dynamic optimization: algorithms and applications
Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given
A Self-Adaptive Discrete PSO Algorithm with Heterogeneous Parameter Values for Dynamic TSP
This paper presents a discrete particle swarm optimization (DPSO) algorithm with heterogeneous (non-uniform) parameter values for solving the dynamic traveling salesman problem (DTSP). The DTSP can be modeled as a sequence of static sub-problems, each of which is an instance of the TSP. In the proposed DPSO algorithm, the information gathered while solving a sub-problem is retained in the form of a pheromone matrix and used by the algorithm while solving the next sub-problem. We present a method for automatically setting the values of the key DPSO parameters (except for the parameters directly related to the computation time and size of a problem).We show that the diversity of parameters values has a positive effect on the quality of the generated results. Furthermore, the population in the proposed algorithm has a higher level of entropy. We compare the performance of the proposed heterogeneous DPSO with two ant colony optimization (ACO) algorithms. The proposed algorithm outperforms the base DPSO and is competitive with the ACO