Eduardo Olguín

Solving the non-unicost set covering problem by using cuckoo search and black hole optimization

The set covering problem is a classical optimization benchmark that finds application in several real-world domains, particularly in line balancing production, crew scheduling, and service installation. The problem consists in finding a subset of columns in a zero-one matrix such that they cover all the rows of the matrix at a minimum cost. In this paper, we present two new approaches for efficiently solving this problem, the first one based on cuckoo search and the second one on black hole optimization. Both are relatively modern bio-inspired metaheuristics that have attracted much attention due to their rapid convergence, easy implementation, and encouraging obtained results. We integrate to the core of both metaheuristics an effective pre-processing phase as well as multiple transfer functions and discretization methods. Pre-processing is employed for filtering the values from domains leading to infeasible solutions, while transfers function and discretization methods are used for efficiently handling the binary nature of the problem. We illustrate interesting experimental results where the two proposed approaches are able to obtain various global optimums for a set of well-known set covering problem instances, outperforming also several recently reported techniques.

Online control of enumeration strategies via bat algorithm and black hole optimization

Constraint programming is an efficient and powerful paradigm for solving constraint satisfaction and optimization problems. Under this paradigm, problems are modeled as a sequence of variables and a set of constraints. The variables have a non-empty domain of candidate values and constraints restrict the values that variables can adopt. The solving process operates by assigning values to variables in order to produce potential solutions which are then evaluated. A main component in this process is the enumeration strategy, which decides the order in which variables and values are chosen to produce such potential solutions. There exist different ways to perform this selection, and depending on the quality of this decision, the efficiency of the solving process may dramatically vary. Unfortunately, selecting the proper strategy is known to be a hard task, as its behavior during search is generally unpredictable and certainly depends on the problem at hand. A recent trend to handle this concern, is to interleave a set of different strategies instead of using a single one during the whole process. The strategies are evaluated according to process indicators in order to use the most promising one on each part of the search process. This process is known as online control of enumeration strategies and its correct configuration can be seen itself as an optimization problem. In this paper, we present two new systems for online control of enumeration strategies based on recent nature-inspired metaheuristics: bat algorithm and black hole optimization. The bat algorithm mimics the location capabilities of bats that employ echoes to identify the objects in their surrounding areas, while black hole optimization inspires its behavior on the gravitational pull of black holes in space. We perform different experimental results by using different enumeration strategies and well-known benchmarks, where the proposed approaches are able to noticeably outperform previous work on online control.

Solving Manufacturing Cell Design Problems by Using a Dolphin Echolocation Algorithm

The Manufacturing Cell Design is a problem that consist in organize machines in cells to increase productivity, i.e., minimize the movement of parts for a given product between machines. In order to solve this problem we use a Dolphin Echolocation algorithm, a recent bio-inspired metaheuristic based on a dolphin feature, the echolocation. This feature is used by the dolphin to search all around the search space for a target, then the dolphin exploits the surround area in order to find promising solutions. Our approach has been tested by using a set of 10 benchmark instances with several configurations, reaching to optimal values for all of them.

Two swarm intelligence algorithms for the Set Covering Problem

The Weighted Set Covering problem is a formal model for many industrial optimization problems. In the Weighted Set Covering Problem the goal is to choose a subset of columns of minimal cost in order to cover every row. Here, we present its resolution with two novel metaheuristics: Firefly Algorithm and Artificial Bee Colony Algorithm. The Firefly Algorithm is inspired by the flashing behaviour of fireflies. The main purpose of flashing is to act as a signal to attract other fireflies. The flashing light can be formulated in such a way that it is associated with the objective function to be optimized. The Artificial Bee Colony Algorithm mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with random search. Experimental results show that both are competitive in terms of solution quality with other recent metaheuristic approaches.

Autonomous Search in Constraint Satisfaction via Black Hole: A Performance Evaluation Using Different Choice Functions

Autonomous Search is a modern technique aimed at introducing self-adjusting features to problem-solvers. In the context of constraint satisfaction, the idea is to let the solver engine to autonomously replace its solving strategies by more promising ones when poor performances are identified. The replacement is controlled by a choice function, which takes decisions based on information collected during solving time. However, the design of choice functions can be done in very different ways, leading of course to very different resolution processes. In this paper, we present a performance evaluation of 16 rigorously designed choice functions. Our goal is to provide new and interesting knowledge about the behavior of such functions in autonomous search architectures. To this end, we employ a set of well-known benchmarks that share general features that may be present on most constraint satisfaction and optimization problems. We believe this information will be useful in order to design better autonomous search systems for constraint satisfaction.

Solving the Set Covering Problem with the Soccer League Competition Algorithm

The Soccer League Competition (SLC) algorithm is a new metaheuristic approach intendended to solve complex optimization problems. It is based in the interaction model present in soccer teams and the goal to win every match, becoming the best team and league of players. This paper presents adaptations to the initial mode of SLC for the purpose of being applied to the Set Covering Problem (SCP) with a Python implementation.

Biogeography-Based Optimization Algorithm for Solving the Set Covering Problem

Biogeography-Based Optimization Algorithm (BBOA) is a kind of new global optimization algorithm inspired by biogeography. It mimics the migration behavior of animals in nature to solve optimization and engineering problems. In this paper, BBOA for the Set Covering Problem (SCP) is proposed. SCP is a classic combinatorial problem from NP-hard list problems. It consist to find a set of solutions that cover a range of needs at the lowest possible cost following certain constraints. In addition, we provide a new feature for improve performance of BBOA, improving stagnation in local optimum. With this, the experiment results show that BBOA is very good at solving such problems.

A 2-level Approach for the Set Covering Problem: Parameter Tuning of Artificial Bee Colony Algorithm by Using Genetic Algorithm

We present a novel application of the Artificial Bee Colony algorithm to solve the non-unicost Set Covering Problem. The Artificial Bee Colony algorithm is a recent Swarm Metaheuristic technique based on the intelligent foraging behavior of honey bees. We present a 2-level metaheuristic approach where an Artificial Bee Colony Algorithm acts as a low-level metaheuristic and its paremeters are set by a higher level Genetic Algorithm.

Agile Software Teams Can Use Conflict to Create a Better Products

Agile Software processes emphasize collaboration more than traditional methods. Collaborations and interactions are cited directly in two of the four values listed in the agile manifesto. Because of everything that involves communication contains the potential for conflict, we are interested in knowing how to manage conflicts to enhance agile projects.

Cat Swarm Optimization with Different Transfer Functions for Solving Set Covering Problems

This work presents a study of a new binary cat swarm optimization. The cat swarm algorithm is a recent swarm metaheuristic technique based on the behaviour of discrete cats. We test the proposed binary cat swarm optimization solving the set covering problem which is a well-known NP-hard discrete optimization problem with many practical applications, such as: political districting, information retrieval, production planning in industry, sector location and fire companies, among others. To tackle the mapping from a continuous search space to a discrete search space we use different transfer functions, S-shaped family and V-shaped family, which are investigated in terms of convergence speed and accuracy of results. The experimental results show the effectiveness of our approach where the binary cat swarm algorithm produce competitive results solving a portfolio of set covering problems from the OR-Library.