Hybrid differential evolution algorithms for the optimal camera placement problem

Purpose – This paper investigates to what extent hybrid differential evolution (DE) algorithms can be successful in solving the optimal camera placement problem. Design/methodology/approach – This problem is stated as a unicost set covering problem (USCP) and 18 problem instances are defined according to practical operational needs. Three methods are selected from the literature to solve these instances: a CPLEX solver, a greedy algorithm, and a row weighting local search (RWLS). Then, it is proposed to hybridize these algorithms with two DE approaches designed for combinatorial optimization problems. The first one is a set-based approach (DEset) from the literature. The second one is a new similarity-based approach (DEsim) that takes advantage of the geometric characteristics of a camera in order to find better solutions. Findings – The experimental study highlights that RWLS and DEsim-CPLEX are the best proposed algorithms. Both easily outperform CPLEX, and it turns out that RWLS performs better on one class of problem instances, whereas DEsim-CPLEX performs better on another class, depending on the minimal resolution needed in practice. Originality/value – Up to now, the efficiency of RWLS and the DEset approach has been investigated only for a few problems. Thus, the first contribution is to apply these methods for the first time in the context of camera placement. Moreover, new hybrid DE algorithms are proposed to solve the optimal camera placement problem when stated as a USCP. The second main contribution is the design of the DEsim approach that uses the distance between camera locations in order to fully benefit from the DE mutation scheme

Insect Foraging of Owl Monkeys

This talk was given during the Animal Behaviour Society Annual Conference

Generalized Relax-and-Fix heuristic

This paper introduces a heuristic for mixed-integer mathematical programs, that can be seen as a generalization of the relax-and-fix heuristic: a sequence of derived subproblems is solved, progressively fixing variables in the original problem. We propose a generic implementation and report on numerical results for four well-known operational research applications: lot-sizing, vehicle routing, bin-packing and portfolio optimization. Results show that this heuristic may be competitive depending on the definition of subproblems

A Memetic Approach for the Unicost Set Covering Problem

International audienceThe Unicost Set Covering Problem (USCP) is a well-known NP-hard combinatorial optimization problem. This paper presents a memetic algorithm that combines and adapts the Hybrid Evolutionary Algorithm in Duet (HEAD) and the Row Weighting Local Search (RWLS) to solve the USCP. The former is a memetic approach with a population of only two individuals which was originally developed to tackle the graph coloring problem. The latter is a heuristic algorithm designed to solve the USCP by using a smart weighting scheme that prevents early convergence and guides the algorithm toward interesting sets. RWLS has been shown to be one of the most effective algorithm for the USCP. In the proposed approach, RWLS is modified to be efficiently used as the local search of HEAD (for exploitation purpose) on the one hand, and also to be used as the crossover (for exploration purpose) on the other hand. The HEAD framework is also adapted to take advantage of the information provided by the weighting scheme of RWLS. The proposed memetic algorithm is compared to RWLS on 98 widely-used benchmark instances (87 from the OR-Library and 11 derived from Steiner triple systems). The experimental study reports competitive results and the proposed algorithm improves the best known solutions for 8 instances