The LBS package - a microcomputer implementation of the Light Beam Search method for the multiple -objective non-linear mathematical programming

The paper presents the LBS package which is a microcomputer implementation of the Light Beam Search method. The software has been designed to support interactive analysis of multiple-objective continuous non-linear mathematical programming problems. At the decision phase of the interactive procedure, a sample of points, composed of the current point and a number of alternative proposals, is presented to the decision maker (DM). The sample is constructed to ensure a relatively easy evaluation of the sample by the DM. To this end an outranking relation is used as a local preference model in a neighborhood of the current point. The outranking relation is used to define a sub-region of the non-dominated set where the sample presented to the DM comes from. The DM has two possibilities to move from one sub-region to another which better fits his/her preferences. The first possibility consists in specifying a new reference point which is then projected onto the non-dominated set in order to find a better non-dominated point. The second possibility consists in shifting the current point to a selected point from the sub-region. In both cases, a new sub-region is defined around the updated current point. This technique can be compared to projecting a focused beam of light from a spotlight at the reference point onto the non-dominated set; the highlighted sub-region changes when either the reference point or the point of interest in the non-dominated set are changed. The LBS package has been implemented in Turbo Pascal within the MS-Windows environment. The package includes two versions of the LBS executable program and a set of example problems. The LBS program is composed of three modules: the problem definition module, the solver module and the interactive analysis module. The problem definition module allows for defining multiple-objective non-linear problems in a natural text form. It supports also checking the correctness of the problem definition and compilation of a problem defined in a text form to an internal format. The solver module is exchangeable and any non-linear optimizer fining to the specified interface can be used in this module. The two versions of the LBS program differ just by the solver used. The first one, coming from the PINOKIO package, is an implementation of the Generalized Reduced Gradient method (GRG). The second one, coming from the DIDAS-N package is an implementation of the Penalty Shifting Method. The interactive analysis module makes an extensive use of computer graphics to help in the perception of a large amount of information. The graphical windows environment allows for simultaneous presentation of different kinds of information and mixing of textual, numerical and graphical forms of presentation

Comparative run-time performance of evolutionary algorithms on multi-objective interpolated continuous optimisation problems.

We propose a new class of multi-objective benchmark problems on which we analyse the performance of four well established multi-objective evolutionary algorithms (MOEAs) – each implementing a different search paradigm – by comparing run-time convergence behaviour over a set of 1200 problem instances. The new benchmarks are created by fusing previously proposed single-objective interpolated continuous optimisation problems (ICOPs) via a common set of Pareto non-dominated seeds. They thus inherit the ICOP property of having tunable fitness landscape features. The benchmarks are of intrinsic interest as they derive from interpolation methods and so can approximate general problem instances. This property is revealed to be of particular importance as our extensive set of numerical experiments indicates that choices pertaining to (i) the weighting of the inverse distance interpolation function and (ii) the problem dimension can be used to construct problems that are challenging to all tested multi-objective search paradigms. This in turn means that the new multi-objective ICOPs problems (MO-ICOPs) can be used to construct well-balanced benchmark sets that discriminate well between the run-time convergence behaviour of different solvers

Using Comparative Preference Statements in Hypervolume-Based Interactive Multiobjective Optimization

International audienceThe objective functions in multiobjective optimization problems are often non-linear, noisy, or not available in a closed form and evolutionary multiobjective optimization (EMO) algorithms have been shown to be well applicable in this case. Here, our objective is to facilitate interactive decision making by saving function evaluations outside the "interesting" regions of the search space within a hypervolume-based EMO algorithm. We focus on a basic model where the Decision Maker (DM) is always asked to pick the most desirable solution among a set. In addition to the scenario where this solution is chosen directly, we present the alternative to specify preferences via a set of so-called comparative preference statements. Examples on standard test problems show the working principles, the competitiveness, and the drawbacks of the proposed algorithm in comparison with the recent iTDEA algorithm

A time predefined variable depth search for nurse rostering

This paper presents a variable depth search for the nurse rostering problem. The algorithm works by chaining together single neighbourhood swaps into more effective compound moves. It achieves this by using heuristics to decide whether to continue extending a chain and which candidates to examine as the next potential link in the chain. Because end users vary in how long they are willing to wait for solutions, a particular goal of this research was to create an algorithm that accepts a user specified computational time limit and uses it effectively. When compared against previously published approaches the results show that the algorithm is very competitive

A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems

This paper presents a new hybrid evolutionary algorithm to solve multi-objective multicast routing problems in telecommunication networks. The algorithm combines simulated annealing based strategies and a genetic local search, aiming at a more flexible and effective exploration and exploitation in the search space of the complex problem to find more non-dominated solutions in the Pareto Front. Due to the complex structure of the multicast tree, crossover and mutation operators have been specifically devised concerning the features and constraints in the problem. A new adaptive mutation probability based on simulated annealing is proposed in the hybrid algorithm to adaptively adjust the mutation rate according to the fitness of the new solution against the average quality of the current population during the evolution procedure. Two simulated annealing based search direction tuning strategies are applied to improve the efficiency and effectiveness of the hybrid evolutionary algorithm. Simulations have been carried out on some benchmark multi-objective multicast routing instances and a large amount of random networks with five real world objectives including cost, delay, link utilisations, average delay and delay variation in telecommunication networks. Experimental results demonstrate that both the simulated annealing based strategies and the genetic local search within the proposed multi-objective algorithm, compared with other multi-objective evolutionary algorithms, can efficiently identify high quality non-dominated solution set for multi-objective multicast routing problems and outperform other conventional multi-objective evolutionary algorithms in the literature

An overview of population-based algorithms for multi-objective optimisation

In this work we present an overview of the most prominent population-based algorithms and the methodologies used to extend them to multiple objective problems. Although not exact in the mathematical sense, it has long been recognised that population-based multi-objective optimisation techniques for real-world applications are immensely valuable and versatile. These techniques are usually employed when exact optimisation methods are not easily applicable or simply when, due to sheer complexity, such techniques could potentially be very costly. Another advantage is that since a population of decision vectors is considered in each generation these algorithms are implicitly parallelisable and can generate an approximation of the entire Pareto front at each iteration. A critique of their capabilities is also provided

Mechanical recycling of polylactide, upgrading trends and combination of valorization techniques

The upcoming introduction of polylactides in the fractions of polymer waste encourages technologists to ascertain its valorization at the best quality conditions. Mechanical recycling of PLA represents one of the most cost-effective methodologies, but the recycled materials are usually directed to downgraded applications, due to the inherent thermomechanical degradation affecting its mechanical, thermal and rheological performance. In this review, the current state of mechanical recycling of PLA is reported, with special emphasis on a multi-scale comparison among different studies. Additionally, the applications of physical and chemical upgrading strategies, as well as the chances to blend and/ or composite recycled PLA are considered. Moreover, the different valorization techniques that can be combined to optimize the value of PLA goods along its life cycle are discussed. Finally, a list of different opportunities to nurture the background of the mechanical recycling of PLA is proposed, in order to contribute to the correct waste management of PLA wastes

Evolving cell models for systems and synthetic biology

This paper proposes a new methodology for the automated design of cell models for systems and synthetic biology. Our modelling framework is based on P systems, a discrete, stochastic and modular formal modelling language. The automated design of biological models comprising the optimization of the model structure and its stochastic kinetic constants is performed using an evolutionary algorithm. The evolutionary algorithm evolves model structures by combining different modules taken from a predefined module library and then it fine-tunes the associated stochastic kinetic constants. We investigate four alternative objective functions for the fitness calculation within the evolutionary algorithm: (1) equally weighted sum method, (2) normalization method, (3) randomly weighted sum method, and (4) equally weighted product method. The effectiveness of the methodology is tested on four case studies of increasing complexity including negative and positive autoregulation as well as two gene networks implementing a pulse generator and a bandwidth detector. We provide a systematic analysis of the evolutionary algorithm’s results as well as of the resulting evolved cell models