Decision-making Tools and Memetic Algorithms in Management and Linear Programming Problems

Operational Research uses a set of tools based on scientific research principles to achieve rational and meaningful management decisions. This article tries to give solution to a highly complex Linear Programming problem by using Simplex method, Solver and a hybrid prototype which combines the theories of Genetic Algorithms with a new local search heuristic technique. Hybridization of these two techniques is becoming known as Memetic Algorithm. Additionally, this article tries to present different techniques to support management decision-making, with the intention of being used increasingly in the business environment sustaining, thus, decisions by mathematics or artificial intelligence and not only by experience.quantitative management; quantitative methods; decision-making; linear programming; operational research; heuristics; hybrid methods; memetic algorithms.

A tutorial for competent memetic algorithms: Model, taxonomy and design issues

The combination of evolutionary algorithms with local search was named "memetic algorithms" (MAs) (Moscato, 1989). These methods are inspired by models of natural systems that combine the evolutionary adaptation of a population with individual learning within the lifetimes of its members. Additionally, MAs are inspired by Richard Dawkin's concept of a meme, which represents a unit of cultural evolution that can exhibit local refinement (Dawkins, 1976). In the case of MA's, "memes" refer to the strategies (e.g., local refinement, perturbation, or constructive methods, etc.) that are employed to improve individuals. In this paper, we review some works on the application of MAs to well-known combinatorial optimization problems, and place them in a framework defined by a general syntactic model. This model provides us with a classification scheme based on a computable index D, which facilitates algorithmic comparisons and suggests areas for future research. Also, by having an abstract model for this class of metaheuristics, it is possible to explore their design space and better understand their behavior from a theoretical standpoint. We illustrate the theoretical and practical relevance of this model and taxonomy for MAs in the context of a discussion of important design issues that must be addressed to produce effective and efficient MAs

An evolutionary non-Linear great deluge approach for solving course timetabling problems

The aim of this paper is to extend our non-linear great deluge algorithm into an evolutionary approach by incorporating a population and a mutation operator to solve the university course timetabling problems. This approach might be seen as a variation of memetic algorithms. The popularity of evolutionary computation approaches has increased and become an important technique in solving complex combinatorial optimisation problems. The proposed approach is an extension of a non-linear great deluge algorithm in which evolutionary operators are incorporated. First, we generate a population of feasible solutions using a tailored process that incorporates heuristics for graph colouring and assignment problems. The initialisation process is capable of producing feasible solutions even for large and most constrained problem instances. Then, the population of feasible timetables is subject to a steady-state evolutionary process that combines mutation and stochastic local search. We conducted experiments to evaluate the performance of the proposed algorithm and in particular, the contribution of the evolutionary operators. The results showed the effectiveness of the hybridisation between non-linear great deluge and evolutionary operators in solving university course timetabling problems

An evolutionary non-Linear great deluge approach for solving course timetabling problems

The aim of this paper is to extend our non-linear great deluge algorithm into an evolutionary approach by incorporating a population and a mutation operator to solve the university course timetabling problems. This approach might be seen as a variation of memetic algorithms. The popularity of evolutionary computation approaches has increased and become an important technique in solving complex combinatorial optimisation problems. The proposed approach is an extension of a non-linear great deluge algorithm in which evolutionary operators are incorporated. First, we generate a population of feasible solutions using a tailored process that incorporates heuristics for graph colouring and assignment problems. The initialisation process is capable of producing feasible solutions even for large and most constrained problem instances. Then, the population of feasible timetables is subject to a steady-state evolutionary process that combines mutation and stochastic local search. We conducted experiments to evaluate the performance of the proposed algorithm and in particular, the contribution of the evolutionary operators. The results showed the effectiveness of the hybridisation between non-linear great deluge and evolutionary operators in solving university course timetabling problems

Memetic Algorithms with Local Search Chains in R: The Rmalschains Package

Global optimization is an important field of research both in mathematics and computer sciences. It has applications in nearly all fields of modern science and engineering. Memetic algorithms are powerful problem solvers in the domain of continuous optimization, as they offer a trade-off between exploration of the search space using an evolutionary algorithm scheme, and focused exploitation of promising regions with a local search algorithm. In particular, we describe the memetic algorithms with local search chains (MA-LS-Chains) paradigm, and the R package Rmalschains, which implements them. MA-LS-Chains has proven to be effective compared to other algorithms, especially in high-dimensional problem solving. In an experimental study, we demonstrate the advantages of using Rmalschains for high-dimension optimization problems in comparison to other optimization methods already available in R.This work was supported in part by the Spanish Ministry of Science and Innovation (MICINN) under Project TIN-2009-14575. The work was performed while C. Bergmeir held a scholarship from the Spanish Ministry of Education (MEC) of the “Programa de Formación del Profesorado Universitario (FPU)”

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

A self-learning particle swarm optimizer for global optimization problems

Copyright @ 2011 IEEE. All Rights Reserved. This article was made available through the Brunel Open Access Publishing Fund.Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means that all particles in a swarm use the same strategy. This monotonic learning pattern may cause the lack of intelligence for a particular particle, which makes it unable to deal with different complex situations. This paper presents a novel algorithm, called self-learning particle swarm optimizer (SLPSO), for global optimization problems. In SLPSO, each particle has a set of four strategies to cope with different situations in the search space. The cooperation of the four strategies is implemented by an adaptive learning framework at the individual level, which can enable a particle to choose the optimal strategy according to its own local fitness landscape. The experimental study on a set of 45 test functions and two real-world problems show that SLPSO has a superior performance in comparison with several other peer algorithms.This work was supported by the Engineering and Physical Sciences Research Council of U.K. under Grants EP/E060722/1 and EP/E060722/2

Genetic algorithms with guided and local search strategies for university course timetabling

This article is posted here with permission from the IEEE - Copyright @ 2011 IEEEThe university course timetabling problem (UCTP) is a combinatorial optimization problem, in which a set of events has to be scheduled into time slots and located into suitable rooms. The design of course timetables for academic institutions is a very difficult task because it is an NP-hard problem. This paper investigates genetic algorithms (GAs) with a guided search strategy and local search (LS) techniques for the UCTP. The guided search strategy is used to create offspring into the population based on a data structure that stores information extracted from good individuals of previous generations. The LS techniques use their exploitive search ability to improve the search efficiency of the proposed GAs and the quality of individuals. The proposed GAs are tested on two sets of benchmark problems in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed GAs are able to produce promising results for the UCTP.This work was supported by the Engineering and Physical Sciences Research Council of U.K. under Grant EP/E060722/1