Query: Multiobjective evolutionary algorithms state of the art

title: On the Effect of the Cooperation of Indicator Based Multiobjective Evolutionary Algorithms
abstract: For almost 20 years, quality indicators (QIs) have promoted the design of new selection mechanisms of multiobjective evolutionary algorithms (MOEAs) Each indicator based MOEA (IB MOEA) has specific search preferences related to its baseline QI, producing Pareto front approximations with different properties. In consequence, an IB MOEA based on a single QI has a limited scope of multiobjective optimization problems (MOPs) in which it is expected to have a good performance. This issue is emphasized when the associated Pareto front geometries are highly irregular. In order to overcome these issues, we propose here an island based multiindicator algorithm (IMIA) that takes advantage of the search biases of multiple IB MOEAs through a cooperative scheme. Our experimental results show that the cooperation of multiple IB MOEAs allows IMIA to perform more robustly (considering several QIs) than the panmictic versions of its baseline IB MOEAs as well as several state of the art MOEAs. Additionally, IMIA shows a Pareto front shape invariance property, which makes it a remarkable optimizer when tackling MOPs with complex Pareto front geometries.

title: Realistic utility functions prove difficult for state of the art interactive multiobjective optimization algorithms
abstract: Improvements to the design of interactive Evolutionary Multiobjective Algorithms (iEMOAs) are unlikely without quantitative assessment of their behaviour in realistic settings. Experiments with human decision makers (DMs) are of limited scope due to the difficulty of isolating individual biases and replicating the experiment with enough subjects, and enough times, to obtain confidence in the results. Simulation studies may help to overcome these issues, but they require the use of realistic simulations of decision makers. Machine decision makers (MDMs) provide a way to carry out such simulation studies, however, studies so far have relied on simple utility functions. In this paper, we analyse and compare two state of the art iEMOAs by means of a MDM that uses a sigmoid shaped utility function. This sigmoid utility function is based on psychologically realistic models from behavioural economics, and replicates several realistic human behaviours. Our findings are that, on a variety of well known benchmarks with two and three objectives, the two iEMOAs do not consistently recover the most preferred points. We hope that these findings provide an impetus for more directed design and analysis of future iEMOAs.

title: An evolutionary parallel multiobjective feature selection framework
abstract: Feature selection has become an indispensable preprocessing step in data mining problems as high amount of data become prevalent with the advances in technology. The objective of feature selection is two fold: reducing data amount and improving learning performance. In this study, we leverage the multi core nature of a regular PC to build a robust framework for feature selection. This framework executes the feature selection algorithm on four processors, in parallel. As per the No Free Lunch Theorem, we facilitate 40 different execution settings for the processors by employing two multiobjective selection algorithms, four initial population generation methods, and five machine learning techniques. Besides, we introduce six setting selection schemes to decide the most fruitful setting for each processor. We carry out extensive experiments on 11 UCI benchmark datasets and analyze the results with statistical tests. Finally, we compare our proposed method with state of the art studies and record remarkable improvement in terms of maximum accuracy.

title: A Hybrid Probabilistic Multiobjective Evolutionary Algorithm for Commercial Recommendation Systems
abstract: As big data driven complex systems, commercial recommendation systems (RSs) have been widely used in such companies as Amazon and Ebay. Their core aim is to maximize total profit, which relies on recommendation accuracy and profits from recommended items. It is also important for them to treat new items equally for a long term run. However, traditional recommendation techniques mainly focus on recommendation accuracy and suffer from a cold start problem (i.e. new items cannot be recommended) Differing from them, this work designs a multiobjective RS by considering item profit and novelty besides accuracy. Then, a hybrid probabilistic multiobjective evolutionary algorithm (MOEA) is proposed to optimize these conflicting metrics. In it, some specifically designed genetic operators are proposed, and two classical MOEA frameworks are adaptively combined such that it owns their complementary advantages. The experimental results reveal that it outperforms some state of the art algorithms as it achieves a higher hypervolume value than them.

title: A Multiobjective State Transition Algorithm Based on Decomposition
abstract: Aggregation functions largely determine the convergence and diversity performance of multi objective evolutionary algorithms in decomposition methods. Nevertheless, the traditional Tchebycheff function does not consider the matching relationship between the weight vectors and candidate solutions. In this paper, the concept of matching degree is proposed which employs vectorial angles between weight vectors and candidate solutions. Based on the matching degree, a new modified Tchebycheff aggregation function is proposed, which integrates matching degree into the Tchebycheff aggregation function. Moreover, the proposed decomposition method has the same functionality with the Tchebycheff aggregation function. Based on the proposed decomposition approach, a new multiobjective optimization algorithm named decomposition based multi objective state transition algorithm is proposed. Relevant experimental results show that the proposed algorithm is highly competitive in comparison with other state of the art multiobjetive optimization algorithms.

title: Balancing Objective Optimization and Constraint Satisfaction in Constrained Evolutionary Multiobjective Optimization.
abstract: Both objective optimization and constraint satisfaction are crucial for solving constrained multiobjective optimization problems, but the existing evolutionary algorithms encounter difficulties in striking a good balance between them when tackling complex feasible regions. To address this issue, this article proposes a two stage evolutionary algorithm, which adjusts the fitness evaluation strategies during the evolutionary process to adaptively balance objective optimization and constraint satisfaction. The proposed algorithm can switch between the two stages according to the status of the current population, enabling the population to cross the infeasible region and reach the feasible regions in one stage, and to spread along the feasible boundaries in the other stage. Experimental studies on four benchmark suites and three real world applications demonstrate the superiority of the proposed algorithm over the state of the art algorithms, especially on problems with complex feasible regions.

title: Large Scale Evolutionary Multiobjective Optimization Assisted by Directed Sampling
abstract: It is particularly challenging for evolutionary algorithms to quickly converge to the Pareto front in large scale multiobjective optimization. To tackle this problem, this article proposes a large scale multiobjective evolutionary algorithm assisted by some selected individuals generated by directed sampling (DS) At each generation, a set of individuals closer to the ideal point is chosen for performing a DS in the decision space, and those nondominated ones of the sampled solutions are used to assist the reproduction to improve the convergence in evolutionary large scale multiobjective optimization. In addition, elitist nondominated sorting is adopted complementarily for environmental selection with a reference vector based method in order to maintain diversity of the population. Our experimental results show that the proposed algorithm is highly competitive on large scale multiobjective optimization test problems with up to 5000 decision variables compared to five state of the art multiobjective evolutionary algorithms.

title: Multiregional co evolutionary algorithm for dynamic multiobjective optimization
abstract: Dynamic multiobjective optimization problems (DMOPs) require Evolutionary algorithms (EAs) to track the time dependent Pareto optimal front (PF) or Pareto optimal set (PS) and provide diversified solutions. Thus, a multiregional co evolutionary dynamic multiobjective optimization algorithm (MRCDMO) is proposed based on the combination of a multiregional prediction strategy (MRP) and a multiregional diversity maintenance mechanism (MRDM) To accurately predict the moving trend of PS, a series of center points in different subregions is used to build a difference model to estimate the new location of center points when an environmental change is detected. To promote the diversity of the population, some diverse individuals are generated within the subregion of the next predicted PS. These two parts of solutions make up the population under a new environment. The performance of our proposed method is validated by comparison with four state of the art EAs on 12 test functions. Experimental results demonstrate that the proposed algorithm can effectively cover the changing PF and efficiently predict the location of the moving PS.

title: Evolutionary Multiobjective Optimization for Automatic Agent Based Model Calibration: A Comparative Study
abstract: Complex problems can be analyzed by using model simulation but its use is not straight forward since modelers must carefully calibrate and validate their models before using them. This is specially relevant for models considering multiple outputs as its calibration requires handling different criteria jointly. This can be achieved using automated calibration and evolutionary multiobjective optimization methods which are the state of the art in multiobjective optimization as they can find a set of representative Pareto solutions under these restrictions and in a single run. However, selecting the best algorithm for performing automated calibration can be overwhelming. We propose to deal with this issue by conducting an exhaustive analysis of the performance of several evolutionary multiobjective optimization algorithms when calibrating several instances of an agent based model for marketing with multiple outputs. We analyze the calibration results using multiobjective performance indicators and attainment surfaces, including a statistical test for studying the significance of the indicator values, and benchmarking their performance with respect to a classical mathematical method. The results of our experimentation reflect that those algorithms based on decomposition perform significantly better than the remaining methods in most instances. Besides, we also identify how different properties of the problem instances (i.e. the shape of the feasible region, the shape of the Pareto front, and the increased dimensionality) erode the behavior of the algorithms to different degrees.

title: An explicit evolutionary approach for multiobjective energy consumption planning considering user preferences in smart homes
abstract: Modern Smart Cities are highly dependent on an efficient energy service since electricity is used in an increasing number of urban activities. In this regard, Time of Use prices for electricity is a widely implemented policy that has been successful to balance electricity consumption along the day and, thus, diminish the stress and risk of shortcuts of electric grids in peak hours. Indeed, residential customers may now schedule the use of deferrable electrical appliances in their smart homes in off peak hours to reduce the electricity bill. In this context, this work aims to develop an automatic planning tool that accounts for minimizing the electricity costs and enhancing user satisfaction, allowing them to make more efficient usage of the energy consumed. The household energy consumption planning problem is addressed with a multiobjective evolutionary algorithm, for which problem specific operators are devised, and a set of state of the art greedy algorithms aim to optimize different criteria. The proposed resolution algorithms are tested over a set of realistic instances built using real world energy consumption data, Time of Use prices from an electricity company, and user preferences estimated from historical information and sensor data. The results show that the evolutionary algorithm is able to improve upon the greedy algorithms both in terms of the electricity costs and user satisfaction and largely outperforms to a large extent the current strategy without planning implemented by users.