Use Of Multi-objective Evolutionary Algorithms Research Articles

A Synergistic Multi-Objective Evolutionary Algorithm with Diffusion Population Generation for Portfolio Problems

When constructing an investment portfolio, it is important to maximize returns while minimizing risks. This portfolio optimization can be considered as a multi-objective optimization problem that is solved by means of multi-objective evolutionary algorithms. The use of multi-objective evolutionary algorithms (MOEAs) provides an effective approach for dealing with the complex data involved in multi-objective optimization problems. However, current MOEAs often rely on a single strategy to obtain optimal solutions, leading to premature convergence and an insufficient population diversity. In this paper, a new MOEA called the Synergistic MOEA with Diffusion Population Generation (DPG-SMOEA) is proposed to address these limitations by integrating MOEAs with diffusion models. To train the diffusion model, a mixed memory pool strategy is optimized, which collects improved solutions from the MOEA/D-AEE, an optimized MOEA, as training samples. The trained model is then used to generate offspring. Considering the cold-start mechanism of the diffusion model, particularly during the training phase where it is not suitable for generating initial offspring, this paper adjusts and optimizes the collaborative strategy to enhance the synergy between the diffusion model and MOEA/D-AEE. Experimental validation of the DPG-SMOEA demonstrates the advantages of using diffusion models in low-dimensional and relatively continuous data analysis. The results show that the DPG-SMOEA performs well on the low-dimensional Hang Seng Index test dataset, while achieving average performance on other high-dimensional datasets, consistent with theoretical predictions. Overall, the DPG-SMOEA achieves better results compared to MOEA/D-AEE and other multi-objective optimization algorithms.

Third-party software library migration at the method-level using multi-objective evolutionary search

Software developers commonly use third-party software libraries to reduce implementation efforts and mitigate errors in their source code while building high-quality and reliable software. To support software evolution, newer libraries are released periodically with added features, improvements in performance as well as critical updates such as bug fixes. Hence, older, existing (source) libraries need to be replaced with their newer, updated (target) counterparts, in a process known as library migration. Typically, library migration is a time-consuming, manual, and error-prone process that requires developers to analyze the source and target library code and its documentation. Specifically, developers examine Application Programming Interface (API) implementations and documentation to replace each source API with a target API without modifying the underlying software functionality. While recent works have used various techniques to recommend suitable target library replacements, these approaches do not generalize well, e.g., when a source library method needs to be replaced by one or more target library APIs or methods. To address this limitation, we propose the use of multi-objective evolutionary algorithms to identify suitable method replacements during library migration. In particular, we formulate library migration at the method-level as a multi-objective combinatorial optimization problem and examine the performance of 7 multi-objective evolutionary algorithms: UNSGAIII, RNSGAII, AGEMOEA, SMSEMOA, NSGAII, IBEA and MOEAD. We use method signature and documentation similarity, and co-occurrence probability to accurately recommend one or more target library methods. We evaluate our approach by conducting an empirical study on 9 popular Java library migrations mined from 57,447 open-source projects on GitHub. Our results demonstrate that UNSGAIII, RNSGAII, AGEMOEA, SMSEMOA, NSGAII, IBEA and MOEAD achieve 90%, 89%, 94%, 90%, 91%, 94%, and 71% precision on average, and 83%, 23%, 58%, 63%, 58%, 60% and 17% average recall respectively. In the interest of reproducibility, we make all code and results publicly available at: http://bit.ly/MOO-api-migration.

Multiple objective optimisation for antenna diversity on airborne platforms

Vehicles such as automobiles, ships, satellites, and aircraft have a limited amount of physical space to install antennas for communications and navigation systems. This is exacerbated by the use of modern materials, like carbon fibre, and that large areas of the vehicles structure cannot be used to mount antenna, due to aerodynamic or other requirements. Therefore, it is necessary to be able to quickly and accurately find the optimum locations to mount a number of antenna systems, in a restricted space, whilst considering a number of different and sometimes contradictory antenna performance parameters. Thus, defining the optimum antenna locations is a multi-objective problem (MOP) and lends itself to the use of multi-objective evolutionary algorithms (MOEA). This paper presents a MOEA methodology that can be used to accurately, quickly, and robustly define the antenna locations. It will also define an appropriate MOEA and the fitness functions for predicting the radio frequency (RF) interoperability/mutual coupling between antenna systems and antenna RF radiation pattern installed performance.

Kinetic facades: An evolutionary-based performance evaluation framework

Multi-functional kinetic facades have great potential to adapt to the environment, reduce building energy consumption, regulate shading and natural ventilation, and improve human thermal and visual comfort. However, due to their complex nature, kinetic facades systems are often associated with highly onerous time-consuming design and fabrication processes, which significantly limit their diffusion and market uptake. This research aims to propose an integrated, systematic framework that supports kinetic facades’ performance design and decision making since projects’ early design stages. The proposed method makes use of Multi-objective Evolutionary Algorithms (MOEA) which have proven successful in providing optimum solutions for complex problems. The efficacy of the framework is then validated based on a case study analysis to improve daylighting performance in an office building context. The results indicate that the integration of the proposed framework can provide a variety of data to support performance evaluation and to identify the most efficient kinetic facades solutions; moreover, this method provided a variety of optimal solutions using Pareto front and the Ranking Method; each being advantageous in a specific set of criteria as evidenced by improved useful daylight illuminance by up to 30%, daylight autonomy by up to 20% while keeping the under-lit and over-lit values as low as under 10%. Finally, future research can focus on the integration of soft computing methods to create a smart and intelligent performance evaluation framework for kinetic facades.

Semantic similarity controllers: On the trade-off between accuracy and interpretability

In recent times, we have seen an explosion in the number of new solutions to address the problem of semantic similarity. In this context, solutions of a neuronal nature seem to obtain the best results. However, there are some problems related to their low interpretability as well as the large number of resources needed for their training. In this work, we focus on the data-driven approach for the design of semantic similarity controllers. The goal is to offer the human operator a set of solutions in the form of a Pareto front that allows choosing the configuration that best suits a specific use case. To do that, we have explored the use of multi-objective evolutionary algorithms that can help find break-even points for the problem of accuracy versus interpretability.

A comparison of multi-objective optimization algorithms for weight setting problems in traffic engineering

Traffic engineering approaches are increasingly important in network management to allow an optimized configuration and resource allocation. In link-state routing, setting appropriate weights to the links is an important and challenging optimization task. Different approaches have been put forward towards this aim, including evolutionary algorithms (EAs). This work addresses the evaluation of a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to minimize network congestion. In both tasks, the optimization considers scenarios where there is a dynamic alteration in the network, with (1) changes in the traffic demand matrices, and (2) link failures. The methods will simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach. Since this leads to a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came naturally; those are compared to a single-objective EA previously proposed by the authors. The results show a remarkable performance and scalability of NSGA-II in the proposed tasks presenting itself as the most promising option for TE.

A novel approach for neuro-fuzzy system-based multi-objective optimization to capture inherent fuzziness in engineering processes

We deal with multi-objective optimization problems in various fields and in some of them, the objectives are found to be conflicting in nature. We obtain multiple optimal or near-optimal solutions of the problem using a multi-objective evolutionary algorithm (MOEA). In this study, an approach is proposed for enhancing the use of MOEA to establish important input–output relationships of some manufacturing processes. In the proposed approach, after getting an initial set of Pareto-front data points through MOEA, the trade-off solutions are used to train a neuro-fuzzy system (NFS) utilizing an EOA. This trained NFS is then used to get a modified Pareto-front and the modified trade-off solutions are clustered using different clustering algorithms. These clustered solutions are then analyzed to establish the relationships among decision variables and objectives. These principles will surely enrich the knowledge of designers and inspire them to apply this approach for a broad range of practical problems. The data related to two different engineering problems are used to show the applicability of the proposed approach.

The Sodalis system and flux balance analysis as a tool for investigating insect-microbe interactions and the evolution of symbioses

The development of new microbial growth and analytical techniques is becoming increasingly relevant in relation to ‘unculturable’ organisms. This may involve the modification of existing methods or the development of new, custom procedures. One important application of this is in the symbiotic bacteria of insects. Symbionts, due to adaptations to their host, are often difficult to culture in vitro. With the growing interest in the use of modified microbiomes to control vector-borne diseases, improved culture techniques that further the understanding of an insect’s microbiome are becoming increasingly important. The tsetse fly, genus Glossina, is the insect vector for Trypanosoma brucei. This parasite is responsible for human African trypanosomiasis (HAT), endemic in sub-Saharan Africa, as well as the wasting disease nagana in cattle. The tsetse’s secondary symbiont, Sodalis glossinidius, provides a unique potential target for reducing the spread of T. brucei. Here, we describe the use of metabolic modelling to design an entirely defined growth medium for S. glossinidius. This medium was used to verify predictions about carbon and nitrogen usage in the symbiont, including amino acid and vitamin auxotrophies. Furthermore, we discuss the use of multiobjective evolutionary algorithms combined with flux balance analysis to investigate computationally the evolution of symbioses, with S. glossinidius and its free-living relative S. praecaptivus as an exemplar. This work not only improves our understanding of the metabolic interactions within the tsetse microbiome, but serves also as a template for future investigations into symbiont evolution.

DISEÑO ÓPTIMO MULTI-OBJETIVO DE EDIFICIOS DE CONCRETO REFORZADO USANDO ALGORITMOS GENÉTICOS

Se muestra el diseño sísmico óptimo de marcos estructurales de concreto reforzado mediante la aplicación de los algoritmos genéticos multi-objetivo, considerando dos objetivos de manera simultánea. El primer objetivo consiste en controlar la distorsión máxima de entrepiso y el segundo en minimizar el costo total estructural del marco. Para ello las estructuras se analizan representando el efecto sísmico a partir de fuerzas laterales. Para lograr un diseño sísmico satisfactorio se utiliza una búsqueda de soluciones basada en algoritmos evolutivos de optimización multi-objetivo denominada Non-Dominated Sorting Genetic Algorithm (NSGA-II). Los resultados obtenidos muestran que los algoritmos genéticos son una herramienta bastante útil para encontrar soluciones de problemas de optimización estructural, y que los diseños obtenidos son adecuados en términos de desempeño sísmico y economía.

Comparison of multi-objective evolutionary algorithms in hybrid Kansei engineering system for product form design

Understanding the affective needs of customers is crucial to the success of product design. Hybrid Kansei engineering system (HKES) is an expert system capable of generating products in accordance with the affective responses. HKES consists of two subsystems: forward Kansei engineering system (FKES) and backward Kansei engineering system (BKES). In previous studies, HKES was based primarily on single-objective optimization, such that only one optimal design was obtained in a given simulation run. The use of multi-objective evolutionary algorithm (MOEA) in HKES was only attempted using the non-dominated sorting genetic algorithm-II (NSGA-II), such that very little work has been conducted to compare different MOEAs. In this paper, we propose an approach to HKES combining the methodologies of support vector regression (SVR) and MOEAs. In BKES, we constructed predictive models using SVR. In FKES, optimal design alternatives were generated using MOEAs. Representative designs were obtained using fuzzy c-means algorithm for clustering the Pareto front into groups. To enable comparison, we employed three typical MOEAs: NSGA-II, the Pareto envelope-based selection algorithm-II (PESA-II), and the strength Pareto evolutionary algorithm-2 (SPEA2). A case study of vase form design was provided to demonstrate the proposed approach. Our results suggest that NSGA-II has good convergence performance and hybrid performance; in contrast, SPEA2 provides the strong diversity required by designers. The proposed HKES is applicable to a wide variety of product design problems, while providing creative design ideas through the exploration of numerous Pareto optimal solutions.

Least Squares Support Vector Machine for Ranking Solutions of Multi-Objective Water Resources Allocation Optimization Models

There is an increasing trend in the use of multi-objective evolutionary algorithms (MOEAs) to solve multi-objective optimization problems of the allocation of water resources. However, typically the outcome is a set of Pareto optimal solutions which make up a trade-off surface between the objective functions. For decision makers to choose a satisfactory alternative from a set of Pareto-optimal solutions, this paper suggests a new method based on least squares support vector machine (LSSVM) and k-means clustering for ranking the optimal solutions for the multi-objective allocation of water resources. First, the k-means clustering method was adopted to reduce the large set of solutions to a few representative solutions. Then, to capture and represent the decision maker's preferences as well as to select the most desirable alternative, the LSSVM method was applied to obtain the utility value for each representative solution. According to the magnitude of the utility values, the final priority orders of the representative solutions were determined. Finally, this methodology was applied to rank the Pareto optimal solution set obtained from the multi-objective optimization problems of water resources allocation for the water-receiving areas of the South-to-North Water Transfer Project in Hebei Province, China. Moreover, the comparisons of the proposed method with the information entropy method and the artificial neural network (ANN) model were given. The results of the comparison indicate that the proposed method has the ability to rank the non-dominated solutions of the multi-objective operation optimization model and that it can be employed for decision-making on water allocation and management in a river basin.

Mean-semivariance portfolio optimization with multiobjective evolutionary algorithms and technical analysis rules

Recent work has been devoted to study the use of multiobjective evolutionary algorithms (MOEAs) in stock portfolio optimization, within a common mean-variance framework. This article proposes the use of a more appropriate framework, mean-semivariance framework, which takes into account only adverse return variations instead of overall variations. It also proposes the use and comparison of established technical analysis (TA) indicators in pursuing better outcomes within the risk-return relation. Results show there is some difference in the performance of the two selected MOEAs – non-dominated sorting genetic algorithm II (NSGA II) and strength pareto evolutionary algorithm 2 (SPEA 2) – within portfolio optimization. In addition, when used with four TA based strategies – relative strength index (RSI), moving average convergence/divergence (MACD), contrarian bollinger bands (CBB) and bollinger bands (BB), the two selected MOEAs achieve solutions with interesting in-sample and out-of-sample outcomes for the BB strategy.

Efficient Portfolio Construction with the Use of Multiobjective Evolutionary Algorithms: Best Practices and Performance Metrics

This paper provides a systematic study of the technologies and algorithms associated with the implementation of multiobjective evolutionary algorithms (MOEAs) for the solution of the portfolio optimization problem. Based on the examination of the state-of-the art we provide the best practices for dealing with the complexities of the constrained portfolio optimization problem (CPOP). In particular, rigorous algorithmic and technical treatment is provided for the efficient incorporation of a wide range of real-world constraints into the MOEAs. Moreover, we address special configuration issues related to the application of MOEAs for solving the CPOP. Finally, by examining the state-of-the-art we identify the most appropriate performance metrics for the evaluation of the relevant results from the implementation of the MOEAs to the solution of the CPOP.

Robust optimization of turbomachinery cascades using inverse methods

It is widely known that real-world airfoils and turbomachinery cascades are subjected to small perturbations in their geometries due to the manufacturing errors and operational and environmental conditions. As a result, the performance requirements of such systems can be significantly affected, leading to poor designs or even severe failures during operation. In this context, the concept of robust optimal design in computational fluid dynamics is very interesting and can be conveniently carried out by using modern multiobjective numerical optimization techniques, in which the interest is to generate optimal and robust aerodynamic shapes that are less sensitive to small perturbations during their useful life. However, the large number of evaluations of the cost functions makes the use of direct methods in the robust optimization of turbomachinery cascades in aerodynamics very costly, sometimes unfeasible. Those difficulties motivate the study reported in this paper, in which a robust optimization strategy by using inverse methods is proposed consisting in the use of multiobjective evolutionary algorithms to be combined with robustness functions specially adapted to turbomachinery cascades.

Applying multiobjective evolutionary algorithms to dynamic software product lines for reconfiguring mobile applications

Mobile applications require dynamic reconfiguration services (DRS) to self-adapt their behavior to the context changes (e.g., scarcity of resources). Dynamic Software Product Lines (DSPL) are a well-accepted approach to manage runtime variability, by means of late binding the variation points at runtime. During the system’s execution, the DRS deploys different configurations to satisfy the changing requirements according to a multiobjective criterion (e.g., insufficient battery level, requested quality of service). Search-based software engineering and, in particular, multiobjective evolutionary algorithms (MOEAs), can generate valid configurations of a DSPL at runtime. Several approaches use MOEAs to generate optimum configurations of a Software Product Line, but none of them consider DSPLs for mobile devices. In this paper, we explore the use of MOEAs to generate at runtime optimum configurations of the DSPL according to different criteria. The optimization problem is formalized in terms of a Feature Model (FM), a variability model. We evaluate six existing MOEAs by applying them to 12 different FMs, optimizing three different objectives (usability, battery consumption and memory footprint). The results are discussed according to the particular requirements of a DRS for mobile applications, showing that PAES and NSGA-II are the most suitable algorithms for mobile environments.

Wind turbine selection for wind farm layout using multi-objective evolutionary algorithms

Wind energy has become the world’s fastest growing energy source. Although wind farm layout is a well known problem, its solution used to be heuristic, mainly based on the designer experience. A key in search trend is to increase power production capacity over time. Furthermore the production of wind energy often involves uncertainties due to the stochastic nature of wind speeds. The addressed problem contains a novel aspect with respect of other wind turbine selection problems in the context of wind farm design. The problem requires selecting two different wind turbine models (from a list of 26 items available) to minimize the standard deviation of the energy produced throughout the day while maximizing the total energy produced by the wind farm. The novelty of this new approach is based on the fact that wind farms are usually built using a single model of wind turbine. This paper describes the usage of multi-objective evolutionary algorithms (MOEAs) in the context of power energy production, selecting a combination of two different models of wind turbine along with wind speeds distributed over different time spans of the day. Several MOEAs variants belonging to the most renowned and widely used algorithms such as SPEA2 NSGAII, PESA and msPEA have been investigated, tested and compared based on the data gathered from Cancun (Mexico) throughout the year of 2008. We have demonstrated the powerful of MOEAs applied to wind turbine selection problem (WTS) and estimate the mean power and the associated standard deviation considering the wind speed and the dynamics of the power curve of the turbines. Among them, the performance of PESA algorithm looks a little bit superior than the other three algorithms. In conclusion, the use of MOEAs is technically feasible and opens new perspectives for assisting utility companies in developing wind farms.

Is Multiobjective Optimization Ready for Water Resources Practitioners? Utility’s Drought Policy Investigation

Is Multiobjective Optimization Ready for Water Resources Practitioners? Utility’s Drought Policy Investigation

Simultaneous topology and sizing optimization of a water distribution network using a hybrid multiobjective evolutionary algorithm

This paper proposes a new direction for design optimization of a water distribution network (WDN). The new approach introduces an optimization process to the conceptual design stage of a WDN. The use of multiobjective evolutionary algorithms (MOEAs) for simultaneous topology and sizing design of piping networks is presented. The design problem includes both topological and sizing design variables while the objective functions are network cost and total head loss in pipes. The numerical technique, called a network repairing technique (NRT), is proposed to overcome difficulties in operating MOEAs for network topological design. The problem is then solved by using a number of established and newly developed MOEAs. Also, two new MOEAs namely multiobjective real code population-based incremental learning (RPBIL) and a hybrid algorithm of RPBIL with differential evolution (termed RPBIL–DE) are proposed to tackle the design problems. The optimum results obtained are illustrated and compared. It is shown that the proposed network repairing technique is an efficient and effective tool for topological design of WDNs. Based on the hypervolume indicator, the proposed RPBIL–DE is among the best MOEA performers.

A parallel evolutionary algorithm for technical market indicators optimization

This paper deals with the optimization of parameters of technical indicators for stock market investment. Price prediction is a problem of great complexity and, usually, some technical indicators are used to predict market trends. The main difficulty in using technical indicators lies in deciding a set of parameter values. We proposed the use of Multi-Objective Evolutionary Algorithms (MOEAs) to obtain the best parameter values belonging to a collection of indicators that will help in the buying and selling of shares. The experimental results indicate that our MOEA offers a solution to the problem by obtaining results that improve those obtained through technical indicators with standard parameters. In order to reduce execution time is necessary to parallelize the executions. Parallelization results show that distributing the workload of indicators in multiple processors to improve performance is recommended. This parallelization has been performed taking advantage of the idle time in a corporate technology infrastructure. We have configured a small parallel grid using the students Labs of a Computer Science University College.

Multiobjective Optimization of a Pin-Fin Heat Sink Using Evolutionary Algorithms

This paper presents the use of multiobjective evolutionary algorithms for the optimal geometrical design of a pin-fin heat sink. The multiobjective design problem is posed to minimize two conflicting objectives: the junction temperature and the fan pumping power of the heat sink. The design variables are mixed integer/continuous. The encoding/decoding process for this mixed integer/continuous design variables is detailed. The multiobjective optimizers employed to solve the design problem are population-based incremental learning, strength Pareto evolutionary algorithm, particles swarm optimization, and archived multiobjective simulated annealing. The approximate Pareto fronts obtained from using the various optimizers are compared based upon the hypervolume and generational distance indicators. From the results, population-based incremental learning (PBIL) outperforms the others. The new design approach is said to be superior to a classical design approach. It is also illustrated that the proposed multiobjective design process leads to better design compared to the current commercial pin-fin heat sinks.