Miguel Ángel Gutiérrez-Andrade

An evolutionary approach for multi-objective vehicle routing problems with backhauls

Display Omitted A multiobjective evolutionary algorithm for vehicle routing problems with backhauls.Our approach equals or improves upon some of the best-known single-objective results.Instances have few bi-objective conflicts; SSMOEA, NSGA-II and MOEA/D perform well.Large tri-objective solution sets and significant differences in both algorithms.Triobjective setting finds comparable solutions to those from bi-objective settings. The vehicle routing problem (VRP) is an important aspect of transportation logistics with many variants. This paper studies the VRP with backhauls (VRPB) in which the set of customers is partitioned into two subsets: linehaul customers requiring a quantity of product to be delivered, and backhaul customers with a quantity to be picked up. The basic VRPB involves finding a collection of routes with minimum cost, such that all linehaul and backhaul customers are serviced. A common variant is the VRP with selective backhauls (VRPSB), where the collection from backhaul customers is optional. For most real world applications, the number of vehicles, the total travel cost, and the uncollected backhauls are all important objectives to be minimized, so the VRPB needs to be tackled as a multi-objective problem. In this paper, a similarity-based selection evolutionary algorithm approach is proposed for finding improved multi-objective solutions for VRPB, VRPSB, and two further generalizations of them, with fully multi-objective performance evaluation.

A reduced formula for the precise number of (0, 1)-matrices in A (R, S)

A formula that calculates the number of n × m matrices in A(R, S) was presented by Wang (Sci. sinica Ser. A 1 (1988) 1). This formula has 2n - 2n variables. Later, in 1998, a reduced formula was proposed by Wang and Zhang, in which the number of involved variables was reduced to only 2n-1 - n. The reduction in the number of variables is important, but it continues being of order 2n. In this paper a new reduced formula is presented. This formula contains only (n - 2)(n - 1)/2 variables, that is, of order n2.

An evolutionary approach to the multi-objective pickup and delivery problem with time windows

The pickup and delivery problem (PDP) has many real-life applications. In this problem there is a customer set which is partitioned into two subsets: the customers requiring an amount of product (delivery) and the customers providing the product (pickup). There is also a set of transportation requests, which specify the quantity of product that has to be picked up from an origin customer and delivered to a destination customer. There exist a number of vehicles available to be used for completing these tasks. PDP consists of finding a collection of routes with minimum cost, such that all transportation request are serviced. Traditionally, the number of routes has been minimized first, and then the travel distance, however, if these objectives are considered to be equally important, the problem can be tackled as a bi-objective problem. Moreover, time is not always directly proportional to distance, thus travel time can also be considered an important criterion to be optimized and, consequently, PDP has to be regarded as a tri-objective problem. In this paper, we solve PDP as a problem with multiple objectives by means of an evolutionary algorithm and evaluate its performance with proper multi-objective performance tools.

An Efficient Algorithm for Unconstrained Optimization

This paper presents an original and efficient PSO algorithm, which is divided into three phases: (1) stabilization, (2) breadth-first search, and (3) depth-first search. The proposed algorithm, called PSO-3P, was tested with 47 benchmark continuous unconstrained optimization problems, on a total of 82 instances. The numerical results show that the proposed algorithm is able to reach the global optimum. This work mainly focuses on unconstrained optimization problems from 2 to 1,000 variables.

Development of seven hybrid methods based on collective intelligence for solving nonlinear constrained optimization problems

Many real-world problems can be seen as constrained nonlinear optimization problems (CNOP). These problems are relevant because they frequently appear in many industry and science fields, promoting, in the last decades, the design and development of many algorithms for solving CNOP. In this paper, seven hybrids techniques, based on particle swarm optimization, the method of musical composition and differential evolution, as well as a new fitness function formulation used to guide the search, are presented. In order to prove the performance of these techniques, twenty-four benchmark CNOP were used. The experimental results showed that the proposed hybrid techniques are competitive, since their behavior is similar to that observed for several methods reported in the specialized literature. More remarkably, new best known are identified for some test instances.

Redistricting in Mexico

Redistricting is the redrawing of the boundaries of legislative districts for electoral purposes in such a way that Federal or state requirements are fulfilled. In 2015 the National Electoral Institute of Mexico carried out the redistricting process of 15 states using a nonlinear programming model where population equality and compactness were considered as conflicting objective functions, whereas other criteria, such as contiguity, were included as constraints. In order to find high quality redistricting plans in acceptable amounts of time, two automated redistricting algorithms were designed: a Simulated Annealing based algorithm, and an Artificial Bee Colony inspired algorithm. Computational results prove that the population based technique is more robust than its counterpart for this kind of problems.

A comparative study of population-based algorithms for a political districting problem

Purpose This paper aims to propose comparing the performance of three algorithms based on different population-based heuristics, particle swarm optimization (PSO), artificial bee colony (ABC) and method of musical composition (DMMC), for the districting problem. Design/methodology/approach In order to compare the performance of the proposed algorithms, they were tested on eight instances drawn from the Mexican electoral institute database, and their respective performance levels were compared. In addition, a simulated annealing-based (simulated annealing – SA) algorithm was used as reference to evaluate the proposed algorithms. This technique was included in this work because it has been used for Federal districting in Mexico since 1994. The performance of the algorithms was evaluated in terms of the quality of the approximated Pareto front and efficiency. Regarding solution quality, convergence and dispersion of the resulting non-dominated solutions were evaluated. Findings The results show that the quality and diversification of non-dominated solutions generated by population-based algorithms are better than those produced by Federal Electoral Institute’s (IFE’s) SA-based technique. More accurately, among population-based techniques, discrete adaptation of ABC and MMC outperform PSO. Originality/value The performance of three population-based techniques was evaluated for the districting problem. In this paper, the authors used the objective function proposed by the Mexican IFE, a weight aggregation function that seeks for a districting plan that represents the best balance between population equality and compactness. However, the weighting factors can be modified by political agreements; thus, the authors decided to produce a set of efficient solutions, using different weighting factors for the computational experiments. This way, the best algorithm will produce high quality solutions no matter the weighting factors used for a real districting process. The computational experiments proved that the proposed artificial bee colony and method of musical composition-based algorithms produce better quality efficient solutions than its counterparts. These results show that population-based algorithms can outperform traditional local search strategies. Besides, as far as we know, this is the first time that the method of musical composition is used for this kind of problems.

FUGA, A FUZZY GREEDY ALGORITHM FOR REDISTRICTING IN MEXICO

Redistricting is the redrawing of the boundaries of legislative districts for electoral purposes in such a way that the generated districts fulfill federal and state requirements such as contiguity, population equality and compactness. Redistricting is a multi-objective problem which has been proved to be NP-hard. In Mexico, the redistricting process has been done using an aggregation function, considering a weighted sum of the objectives. However, if different weighting factors are used then a set of diverse, high quality solutions can be generated and a new problem arises: which solution should be implemented? In this paper we propose a novel alternative, called FuGA, to select the best solution for the redistricting problem using a fuzzyfication of the objective function. The proposed algorithm was applied in a real case, and its solutions were compared with those produced by VIKOR, a well-known algorithm for decision making. FuGA showed a better performance since it was able to avoid the selection of dominated solutions.