Multi-objective Evolutionary Programming Research Articles

Shear strength assessment of reinforced recycled aggregate concrete beams without stirrups using soft computing techniques

This paper presents a study to predict the shear strength of reinforced recycled aggregate concrete beams without stirrups using soft computing techniques. The methodology involves the development of a Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR) and Gene Expression Programming (GEP) models. The input variables considered are the longitudinal reinforcement ratio, recycled coarse aggregate ratio, beam cross-section dimensions, and concrete compressive strength. Data collected from the literature were used to train and validate the models. The results showed that the MOGA-EPR and GEP models can accurately predict the shear strength of beams without stirrups. The models also performed better than equations from the codes and literature. This study provides an alternative approach to accurately predict the shear strength of reinforced recycled aggregate concrete beams without stirrups.

Application of soft computing in predicting the compressive strength of self-compacted concrete containing recyclable aggregate

AbstractSelf-compacting concrete (SCC) is a type of concrete known for its environmental benefits and improved workability. In this study, data-driven approaches were used to anticipate the compressive strength (CS) of self-compacting concrete (SCC) containing recycled plastic aggregates (RPA). A database of 400 experimental data sets was used to assess the capabilities of multi-objective genetic algorithm evolutionary polynomial regression (MOGA-EPR) and gene expression programming (GEP). The analysis results indicated that the proposed equations provided more accurate CS predictions than traditional approaches such as the linear regression model (LRM). The proposed equations achieved lower mean absolute error (MAE) and root mean square error (RMSE) values, a mean close to the optimum value (1.0), and a higher coefficient of determination (R2) than the LRM. As such, the proposed approaches can be utilized to obtain more reliable design calculations and better predictions of CS in SCC incorporating RPA.

Multi-Objective Immune-Commensal-Evolutionary Programming for Total Production Cost and Total System Loss Minimization via Integrated Economic Dispatch and Distributed Generation Installation

Economic Dispatch (ED) problems have been solved using single-objective optimization for so long, as Grid System Operators (GSOs) previously only focused on minimizing the total production cost. In modern power systems, GSOs require not only optimizing the total production cost but also, at the same time, optimizing other important objectives, such as the total emissions of the greenhouse gasses, total system loss and voltage stability. This requires a suitable multi-objective optimization approach in ensuring the ED solution produced is satisfying all the objectives. This paper presents a new multi-objective optimization technique termed Multi-Objective Immune-Commensal-Evolutionary Programming (MOICEP) for minimizing the total production cost and total system loss via integrated Economic Dispatch and Distributed Generation installation (ED-DG). This involved the application of a weighted-sum multi-objective approach that combined with an optimization technique called Immune-Commensal-Evolutionary Programming (ICEP). The proposed MOICEP has been compared with other multi-objective techniques, which are Multi-Objective-Evolutionary Programming (MOEP) and Multi-Objective-Artificial Immune System (MOAIS). It was found that MOICEP performs very well in producing better optimization results for all the three types of Economic Dispatch (ED) problems compared to MOEP and MOAIS in terms of cheap total production costs and low total system loss.

Modified multi-objective evolutionary programming algorithm for solving project scheduling problems

Though the Genetic Algorithm (GA) has received considerable attention recently in solving multi-objective optimization problems, inefficiency regarding performance has been reported in applications related to project scheduling. The degradation in efficiency was magnificent in applications of highly epistatic objective functions, including scheduling problems wherein the parameters being optimized are highly correlated. Furthermore, the crossover, being the dominant operator in GA, added significantly to the observed inefficiency for causing violations in the dependency between activities. Unlike GA, the Evolutionary Programming (EP) algorithm employs only a mutation operator which makes it less vulnerable to the dependency violation issue. This study proposes a modified Multi-Objective Evolutionary Programming (MOEP) algorithm to model and solve scheduling problems of multi-mode activities, including time–cost trade-off and finance-based scheduling with resource levelling. The modification involves the implementation of a new mutation operator to accommodate the scheduling problems in hand. Furthermore, the modified MOEP algorithm is benchmarked against the two multi-objective algorithms of SPEA-II and NSGA-II which have been used extensively in the literature to solve project scheduling problems. The results indicated that the modified MOEP algorithm outperformed SPEA-II and NSGA-II in terms of the diversity and quality of the Pareto optimal set.

Multi-Objective Evolutionary Programming for Developing Recommender Systems based on Collaborative Filtering

In the era of internet, several online platforms offer many items to users. Users could spend a lot of time to find (or not) some items they are interested, sometimes, they will probably not find the desired items. An effective strategy to overcome this problem is a recommender system, one of the most popular applications of machine learning. Recommender systems select most appropriate items to an specific user based on previous information between items and users, and they are developed using diffeent approaches. One of the most successful approach for developing recommender systems is collaborative filtering, which can filter out items that a user might like based on reactions of users with similar profiles. Often, traditional recommender systems only consider precision as evaluation metric of performance, however, others metrics (like recall, diversity, novelty, etc) are also important. Unfortunately, some metrics are conflicting, e.g., precision impacts negatively on other metrics. This paper presents a multi-objective evolutionary programming method for developing a recommender system, which is based on a new collaborative filtering technique, while maximizes the recall for a given precision, The new collaborative filtering technique uses three components for recommending an item to a user: 1) clustering of users; 2) a previous memory-based prediction; and 3) five decimal parameters (threshold average clustering, threshold penalty, threshold incentive, weight attached to average clustering and weight attached to Pearson correlation). The multiobjective evolutionary programming optimizes the clustering of users and the five decimal parameters, while, it searches maximizes both similarity precision and recall objectives. A comparison between the proposed method and a previous nonevolutionary method shows that the proposed method improves precision and recall metric on a benchmark database.

Multi-Objective Evolutionary Programming for Solving Economic Dispatch Problem

Multi-Objective Evolutionary Programming for Solving Economic Dispatch Problem

Pareto optimal approach in Multi-Objective Chaotic Mutation Immune Evolutionary Programming (MOCMIEP) for optimal Distributed Generation Photovoltaic (DGPV) integration in power system

Abstract The latest advancement in the technology, including the integration of the renewable energy resources, has become a recent trend in the power system infrastructure. Although, this can bring many benefits, excessive integration without proper planning may lead to unwanted circumstances such as voltage instability and higher power losses. This paper proposes a new Pareto optimality based technique namely: Multi-objective Chaotic Mutation Immune Evolutionary Programming. The technique was developed to determines the optimal location and sizing of Distributed Generated Photovoltaic (DGPV) and minimizing the multiple objective functions, namely, the active power losses and Fast Voltage Stability Index (FVSI), simultaneously. The method was tested on the IEEE test system. The results revealed that the proposed technique had the ability to acquire a set of Pareto solutions. Furthermore, this paper also confirmed that Multi-objective Chaotic Mutation Immune Evolutionary Programming (MOCMIEP) outperformed the Multi-objective Evolutionary Programming and Multi-objective Artificial Immune System in most cases.

Multi-Objective Evolutionary Programming for Static VAR Compensator (SVC) in Power System Considering Contingencies (N-m)

<span>Static VAR Compensators (SVCs) is a Flexible Alternating Current Transmission System (FACTS) device that can control the power flow in transmission lines by injecting capacitive or inductive current components at the midpoint of interconnection line or in load areas. This device is capable of minimizing the overall system losses and concurrently improves the voltage stability. A line index, namely <em>SVSI</em> becomes indicator for the placement of SVC and the parameters of SVCs are tuned by using the multi-objective evolutionary programming technique, effectively able to control the power. The algorithm was tested on IEEE-30 Bus Reliability Test System (RTS). Comparative studies were conducted based on the performance of SVC in terms of their location and sizing for installations in power system.</span>

Multi-objective differential evolution based on normalization and improved mutation strategy

Developing efficient algorithms for solving multi-objective optimization problems is a challenging and essential task in many applications. This task involves two or more conflicting objectives that need to be simultaneously optimized. Many real-world problems fall into this category. We introduce an improved version of multi-objective differential evolution (DE) algorithm, namely MOnDE that uses a new mutation strategy and a normalization method to select non-dominated solutions. The new mutation strategy “DE/rand-to-nbest” uses the best normalized individual in terms of all the objectives to guide the search towards the true pareto optimal solutions. As a result, the probability of producing superior solutions is increased and a faster convergence is achieved. Summation of normalized objective values method is used instead of non-domination sorting to overcome the high computational complexity and overhead problems of sorting non-dominated solutions. The performance of our approach is tested on a set of benchmark problems that consist of two to five objectives. Different combinations of multi-objective evolutionary programming and multi-objective differential evolution algorithms have been used for comparisons. The results affirm the efficiency and robustness of the proposed approach among other well-known algorithms from the literature.

Multi-objective optimisation and decision-making of space station logistics strategies

ABSTRACTSpace station logistics strategy optimisation is a complex engineering problem with multiple objectives. Finding a decision-maker-preferred compromise solution becomes more significant when solving such a problem. However, the designer-preferred solution is not easy to determine using the traditional method. Thus, a hybrid approach that combines the multi-objective evolutionary algorithm, physical programming, and differential evolution (DE) algorithm is proposed to deal with the optimisation and decision-making of space station logistics strategies. A multi-objective evolutionary algorithm is used to acquire a Pareto frontier and help determine the range parameters of the physical programming. Physical programming is employed to convert the four-objective problem into a single-objective problem, and a DE algorithm is applied to solve the resulting physical programming-based optimisation problem. Five kinds of objective preference are simulated and compared. The simulation results indicate that the proposed approach can produce good compromise solutions corresponding to different decision-makers’ preferences.

Constrained Reactive Power Control (CRPC) Based Multi-Objective Optimization under Multi-Contingencies (<i>N-m</i>)

Optimal Reactive Power Dispatch (ORPD) is needed for reduces the power system losses and improves voltage profile, power system security and overall power system operation. In this paper, the ORPD problem solved using Constrained Reactive Power Control (CRPC) based Multi-Objective Evolutionary Programming (MOEP) optimization technique considering multi-contingencies (N-m). The proposed technique determines the optimum reactive power to be dispatched by the generators in order to improve voltage stability condition of a system. A computer program was written in MATLAB and the proposed technique was tested on the IEEE 30-bus RTS. Hence, the result was compared with Multi-Objective Artificial Immune System (MOAIS) to highlight it merits.

MOEPGA: A novel method to detect protein complexes in yeast protein–protein interaction networks based on MultiObjective Evolutionary Programming Genetic Algorithm

The identification of protein complexes in protein–protein interaction (PPI) networks has greatly advanced our understanding of biological organisms. Existing computational methods to detect protein complexes are usually based on specific network topological properties of PPI networks. However, due to the inherent complexity of the network structures, the identification of protein complexes may not be fully addressed by using single network topological property. In this study, we propose a novel MultiObjective Evolutionary Programming Genetic Algorithm (MOEPGA) which integrates multiple network topological features to detect biologically meaningful protein complexes. Our approach first systematically analyzes the multiobjective problem in terms of identifying protein complexes from PPI networks, and then constructs the objective function of the iterative algorithm based on three common topological properties of protein complexes from the benchmark dataset, finally we describe our algorithm, which mainly consists of three steps, population initialization, subgraph mutation and subgraph selection operation. To show the utility of our method, we compared MOEPGA with several state-of-the-art algorithms on two yeast PPI datasets. The experiment results demonstrate that the proposed method can not only find more protein complexes but also achieve higher accuracy in terms of fscore. Moreover, our approach can cover a certain number of proteins in the input PPI network in terms of the normalized clustering score. Taken together, our method can serve as a powerful framework to detect protein complexes in yeast PPI networks, thereby facilitating the identification of the underlying biological functions.

Three-objective subgraph mining using multiobjective evolutionary programming

The existing methods for graph-based data mining (GBDM) follow the basic approach of applying a single-objective search with a user-defined threshold to discover interesting subgraphs. This obliges the user to deal with simple thresholds and impedes her/him from evaluating the mined subgraphs by defining different “goodness” (i.e., multiobjective) criteria regarding the characteristics of the subgraphs. In previous papers, we defined a multiobjective GBDM framework to perform bi-objective graph mining in terms of subgraph support and size maximization. Two different search methods were considered with this aim, a multiobjective beam search and a multiobjective evolutionary programming (MOEP). In this contribution, we extend the latter formulation to a three-objective framework by incorporating another classical graph mining objective, the subgraph diameter. The proposed MOEP method for multiobjective GBDM is tested on five synthetic and real-world datasets and its performance is compared against single and multiobjective subgraph mining approaches based on the classical Subdue technique in GBDM. The results highlight the application of multiobjective subgraph mining allows us to discover more diversified subgraphs in the objective space.

A multiobjective evolutionary programming framework for graph-based data mining

Subgraph mining is the process of identifying concepts describing interesting and repetitive subgraphs within graph-based data. The exponential number of possible subgraphs makes the problem very challenging. Existing methods apply a single-objective subgraph search with the view that interesting subgraphs are those capable of not merely compressing the data, but also enhancing the interpretation of the data considerably. Usually the methods operate by posing simple constraints (or user-defined thresholds) such as returning all subgraphs whose frequency is above a specified threshold. Such search approach may often return either a large number of solutions in the case of a weakly defined objective or very few in the case of a very strictly defined objective. In this paper, we propose a framework based on multiobjective evolutionary programming to mine subgraphs by jointly maximizing two objectives, support and size of the extracted subgraphs. The proposed methodology is able to discover a nondominated set of interesting subgraphs subject to tradeoff between the two objectives, which otherwise would not be achieved by the single-objective search. Besides, it can use different specific multiobjective evolutionary programming methods. Experimental results obtained by three of the latter methods on synthetically generated as well as real-life graph-based datasets validate the utility of the proposed methodology when benchmarked against classical single-objective methods and their previous, nonevolutionary multiobjective extensions.

Multiobjective Optimization Methods for Congestion Management in Deregulated Power Systems

Congestion management is one of the important functions performed by system operator in deregulated electricity market to ensure secure operation of transmission system. This paper proposes two effective methods for transmission congestion alleviation in deregulated power system. Congestion or overload in transmission networks is alleviated by rescheduling of generators and/or load shedding. The two objectives conflicting in nature (1) transmission line over load and (2) congestion cost are optimized in this paper. The multiobjective fuzzy evolutionary programming (FEP) and nondominated sorting genetic algorithm II methods are used to solve this problem. FEP uses the combined advantages of fuzzy and evolutionary programming (EP) techniques and gives better unique solution satisfying both objectives, whereas nondominated sorting genetic algorithm (NSGA) II gives a set of Pareto‐optimal solutions. The methods propose an efficient and reliable algorithm for line overload alleviation due to critical line outages in a deregulated power markets. The quality and usefulness of the algorithm is tested on IEEE 30 bus system.

Comparison of evolutionary computation algorithms for solving bi-objective task scheduling problem on heterogeneous distributed computing systems

The task scheduling problem in heterogeneous distributed computing systems is a multiobjective optimization problem (MOP). In heterogeneous distributed computing systems (HDCS), there is a possibility of processor and network failures and this affects the applications running on the HDCS. To reduce the impact of failures on an application running on HDCS, scheduling algorithms must be devised which minimize not only the schedule length (makespan) but also the failure probability of the application (reliability). These objectives are conflicting and it is not possible to minimize both objectives at the same time. Thus, it is needed to develop scheduling algorithms which account both for schedule length and the failure probability. Multiobjective Evolutionary Computation algorithms (MOEAs) are well-suited for Multiobjective task scheduling on heterogeneous environment. The two Multi-Objective Evolutionary Algorithms such as Multiobjective Genetic Algorithm (MOGA) and Multiobjective Evolutionary Programming (MOEP) with non-dominated sorting are developed and compared for the various random task graphs and also for a real-time numerical application graph. The metrics for evaluating the convergence and diversity of the obtained non-dominated solutions by the two algorithms are reported. The simulation results confirm that the proposed algorithms can be used for solving the task scheduling at reduced computational times compared to the weighted-sum based biobjective algorithm in the literature.

Multi-objective evolutionary algorithms based on the summation of normalized objectives and diversified selection

Most multi-objective evolutionary algorithms (MOEAs) use the concept of dominance in the search process to select the top solutions as parents in an elitist manner. However, as MOEAs are probabilistic search methods, some useful information may be wasted, if the dominated solutions are completely disregarded. In addition, the diversity may be lost during the early stages of the search process leading to a locally optimal or partial Pareto-front. Beside this, the non-domination sorting process is complex and time consuming. To overcome these problems, this paper proposes multi-objective evolutionary algorithms based on Summation of normalized objective values and diversified selection (SNOV-DS). The performance of this algorithm is tested on a set of benchmark problems using both multi-objective evolutionary programming (MOEP) and multi-objective differential evolution (MODE). With the proposed method, the performance metric has improved significantly and the speed of the parent selection process has also increased when compared with the non-domination sorting. In addition, the proposed algorithm also outperforms ten other algorithms.

A Multiobjective Evolutionary Programming Algorithm and Its Applications to Power Generation Expansion Planning

The generation expansion planning (GEP) problem is defined as the problem of determining WHAT, WHEN, and WHERE new generation units should be installed over a planning horizon to satisfy the expected energy demand. This paper presents a framework to determine the number of new generating units (e.g., conventional steam units, coal units, combined cycle modules, nuclear plants, gas turbines, wind farms, and geothermal and hydro units), power generation capacity for those units, number of new circuits on the network, the voltage phase angle at each node, and the amount of required imported fuel for a single-period generation expansion plan. The resulting mathematical program is a mixed-integer bilinear multiobjective GEP model. The proposed framework includes a multiobjective evolutionary programming algorithm to obtain an approximation of the Pareto front for the multiobjective optimization problem and analytical hierarchy process to select the best alternative. A Mexican power system case study is utilized to illustrate the proposed framework. Results show coherent decisions given the objectives and scenarios considered. Some sensitivity analysis is presented when considering different fuel price scenarios.

A multi-objective evolutionary programming approach to the ‘object location’ spatial analysis and optimisation problem within the urban water management domain

Evolutionary programming (EP) is an application of the concepts of Darwinian evolution to complex optimisation problems. This is primarily addressed in the literature through the use of genetic algorithms (GAs), but there are problems where a hybrid approach coupling the robustness of GAs with the effectiveness of a heuristic procedure may yield better results. This paper focusses on the development and use of such a hybrid EP algorithm to solve a particular multi-objective spatial object-location problem. The domain knowledge which forms part of the heuristics of the methodology developed is provided by the problem of citing sustainable water management strategies within the urban fabric, taking into account social, economic, technical and cost parameters and constraints.

Optimal narrowband dispersionless fiber Bragg grating filters with short grating length and smooth dispersion profile

An effective optimization approach to the inverse design problems of complex fiber Bragg grating filters is developed in the present paper. Based on a multi-objective evolutionary programming (MOEP) algorithm, the proposed method can efficiently search for optimal solutions and simultaneously take into account various requirements of the designed filter. To improve the efficiency of the MOEP based algorithm, an adaptive mutation process is proposed and verified. One of the advantages of the proposed optimization method is the capability to impose additional constrains on the desired coupling coefficient, which ensures the convenience and possibility for actually fabricating the designed devices with the commercially available photosensitive fibers. To verify the effectiveness of the proposed method, an optimal narrowband dispersionless fiber Bragg grating filter for DWDM optical fiber communication systems is designed. We successfully demonstrate that complicated dispersionless FBG filters with short grating lengths and smooth dispersion profiles can be obtained by using the proposed algorithm.