Objective Evolutionary Algorithm Research Articles

Hybrid multiple objective evolutionary algorithms for optimising multi-mode time, cost and risk trade-off problem

Identifying and minimising the risks associated with time, and cost factors in construction projects are the main challenges for all parties involved. The objective of project management is to complete the scope of work on time, within budget and deliver a quality product in a safe fashion to maximise overall project success. This research presents a new hybrid multiple objective evolutionary algorithm based on hybridisation of Artificial Bee Colony (ABC) and differential evolution to facilitate time-cost-risk trade-off problems (MOABCDE-TCR). The proposed algorithm integrates core operations from Differential Evolution (DE) into the original ABC in order to enhance the exploration and exploitation capacity of the optimisation process. A numerical construction project case study demonstrates the ability of MOABCDE-generated non-dominated solutions to optimise TCR problem. Comparisons between the MOABCDE and currently widely used multiple objective algorithms verify the efficiency and effectiveness of the developed algorithm.

Spam Classification Using MOEA/D

In mathematics, it’s very easy to find the maximum point or minimum point of a function or a set of functions, but it’s difficult to find a set of function simultaneously in the real world due to the different kinds of mathematical relationships between objective functions. So the multi objective optimization algorithm has the ability to deal with a many objectives instead of one objective, because of the difficulties in the classical methods of multi objectives optimization, the evolutionary algorithm (EA) is effective to eliminate these difficulties, in order to apply the evolutionary algorithms to improve the multi-objective optimization algorithm, the multi - objective evolutionary algorithm based on decomposition is one of the algorithms that solve multi objective optimization problems. This paper aims to enhance the e-mail spam filtering by using multi - objective evolutionary algorithm for classifying the e-mail messages to spam or non-spam in high accuracy. The first step in the proposal is applying normalization. The second step is applying feature selection which is implemented to choose the best features. Finally, implement multi - objective evolutionary algorithm based on decomposition. The evaluation of the performance of model by using testing databases from the spam database. The model depended accuracy as a criterion to evaluate model performance. The experimental results showed that the proposed system provides good accuracy in the experiment 1 (91%), very good accuracy in the experiment 2 (92%) and excellent accuracy in the experience 3 (98%).

Redundant placement of phasor measurement unit using multi objective evolutionary algorithms based on modified spectral clustering

Optimal placement of phasor measurement unit (OPPMU) considering controlled islanding is a high dimensional constrained optimization problem that determines optimum location of PMU. Controlled islanding can be used as an effective emergency action to split a large scale power system into islands for avoiding blackouts. This paper proposes an optimal PMU placement model considering power system controlled islanding. So that the power network remains observable under controlled islanding as well as normal operating condition. A new method based on spectral clustering is proposed to handle multiple constraints in order to guarantee the stability of generated islands. The objective function used in this controlled islanding algorithm is the minimal power flow disruption. Constraints such as generator coherency, load–generation imbalance are considered for spectral clustering. OPPMU is formed for the obtained islands using two conflicting objectives of minimizing the number of PMU and maximizing the measurement redundancy using conventional mixed integer linear programming, real coded genetic algorithm(RGA), non dominated sorting differential evolutionary algorithm, and modified non dominated sorting GA II (MNSGA II). RGA with simulated binary crossover and non dominated sorting differential evolution with improved crowding distance and mutation is implemented. In MNSGA II proposed a variant which reduces the run-time complexity using the technique space–time-trade-off. Simulation results using NE 39 and IEEE 118 bus test systems show that the proposed method is computationally efficient when solving controlled islanding based OPPMU problem.

Multi objective evolutionary algorithm for designing energy efficient distribution transformers

Abstract This paper has solved the transformer design optimization problem using Multi-Objective Evolutionary Algorithms based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA). For lesser computation burden, the existing design techniques merely employ few Standard Design Variables (SDV), satisfying only a few performance constraints, resulting in an approximated design, without any focus on an energy efficient transformer. The proposed methodology minimizes four sets of conflicting design bi-objectives, subjected to 27 constraints, incorporating three crucial design variables with SDV to ensure energy efficient transformer design with lesser losses, total life time cost (TLTC), green house gas emission, and failure rate. Different cases are analysed on a sample 1500kVA transformer, which is designed by existing technique and the proposed multi objective optimization problem formulation approach and the performances of the competing transformers are compared. To prove the effectiveness of Iterative Chaotic map with infinite collapses assisted MOEA/D-DRA (ICMDRA), NSGA-II has also been successfully applied to solve the problem. When tested in all three different rating transformers, the simulation results have proved that the proposed methodology saves energy, cost, and material, with ICMDRA rather than NSGA-II. This paper identifies ICMDRA as a superior algorithm for transformer design, in terms of diversity and convergence. Also, the core loss calculation of the transformer designed using the proposed methodology is validated by 3D-FEM assessment and experimental prototype setup for a 200kVA transformer.

Efficient load aware evolutionary routing protocol in hybrid wireless network using MOEAD/D

The hybrid wireless network has developed as a promising solution, enabling mobile customers to accomplish higher execution and access service in a consistent way free of their reality in the range of wireless local area network (WLAN). In this paper, we have proposed the load aware routing protocol for the hybrid wireless network. Hybrid networks were having the combination of several networks such as MANET and VANET. In the hybrid network generally, the data transmission is very high and hence, it is necessary to create a reliable routing protocol for avoiding delay and to increase packet delivery ratio. Thus, we have implemented the optimal algorithm of MOEAD/D, i.e., a multi objective evolutionary algorithm based on decomposition combined with Djikstra. Here the MOEAD is used to find the best router for transmitting the data packets and the Djikstra algorithm is used to find the shortest path for the router. Thus, this combination of MOEAD and Djikstra has yielded a reliable load aware routing protocol. The simulation result shows that the performance of our proposed routing protocol is better than the existing protocol.

Efficient load aware evolutionary routing protocol in hybrid wireless network using MOEAD/D

The hybrid wireless network has developed as a promising solution, enabling mobile customers to accomplish higher execution and access service in a consistent way free of their reality in the range of wireless local area network (WLAN). In this paper, we have proposed the load aware routing protocol for the hybrid wireless network. Hybrid networks were having the combination of several networks such as MANET and VANET. In the hybrid network generally, the data transmission is very high and hence, it is necessary to create a reliable routing protocol for avoiding delay and to increase packet delivery ratio. Thus, we have implemented the optimal algorithm of MOEAD/D, i.e., a multi objective evolutionary algorithm based on decomposition combined with Djikstra. Here the MOEAD is used to find the best router for transmitting the data packets and the Djikstra algorithm is used to find the shortest path for the router. Thus, this combination of MOEAD and Djikstra has yielded a reliable load aware routing protocol. The simulation result shows that the performance of our proposed routing protocol is better than the existing protocol.

Balancing exploration, uncertainty and computational demands in many objective reservoir optimization

Abstract Reservoir operations are central to our ability to manage river basin systems serving conflicting multi-sectoral demands under increasingly uncertain futures. These challenges motivate the need for new solution strategies capable of effectively and efficiently discovering the multi-sectoral tradeoffs that are inherent to alternative reservoir operation policies. Evolutionary many-objective direct policy search (EMODPS) is gaining importance in this context due to its capability of addressing multiple objectives and its flexibility in incorporating multiple sources of uncertainties. This simulation-optimization framework has high potential for addressing the complexities of water resources management, and it can benefit from current advances in parallel computing and meta-heuristics. This study contributes a diagnostic assessment of state-of-the-art parallel strategies for the auto-adaptive Borg Multi Objective Evolutionary Algorithm (MOEA) to support EMODPS. Our analysis focuses on the Lower Susquehanna River Basin (LSRB) system where multiple sectoral demands from hydropower production, urban water supply, recreation and environmental flows need to be balanced. Using EMODPS with different parallel configurations of the Borg MOEA, we optimize operating policies over different size ensembles of synthetic streamflows and evaporation rates. As we increase the ensemble size, we increase the statistical fidelity of our objective function evaluations at the cost of higher computational demands. This study demonstrates how to overcome the mathematical and computational barriers associated with capturing uncertainties in stochastic multiobjective reservoir control optimization, where parallel algorithmic search serves to reduce the wall-clock time in discovering high quality representations of key operational tradeoffs. Our results show that emerging self-adaptive parallelization schemes exploiting cooperative search populations are crucial. Such strategies provide a promising new set of tools for effectively balancing exploration, uncertainty, and computational demands when using EMODPS.

An effective hybrid multi objective evolutionary algorithm for solving real time event in flexible job shop scheduling problem

This paper addresses the multi-objective model for a flexible job shop scheduling problem (FJSSP) to improve the system performance under the condition of machines break down asa real time event. It is important to identify the relevant performance measures to the mentioned problem for examining the system performance. Therefore, minimization of make span and minimization of total machine load variation is considered as two performance measures. Generally, it is very difficult to develop a mathematical model for the real-time situations in FJSSP. Hence, in this paper we divided the research work into two folds: Primarily, a mixed-integer non-linear programming (MINLP) model has been developed to represent the above-mentioned multi-objectives that subjected to constraints without considering machines break down. Secondarily, by incorporating the machines break down as the real-time event the performance of the system is examined. Solving conflicting objectives simultaneously for finding the optimal/near optimal solutions in a reasonable time is a challenge. In this paper, we proposed a new evolutionary based multi-objective teacher learning-based optimization algorithm (MOTLBO) to solve the above-mentioned complex problem. Moreover, to improve the obtained solutions a local search technique has been incorporated in the MOTLBO and comparisons has been made with existing multi-objective particle swarm optimization (MOPSO) and conventional non-dominated sorting genetic algorithm (CNSGA-II). Results found that the proposed multi-objective-based hybrid meta-heuristic algorithm produced high-quality solutions as proved by the tests we performed over a number of randomly generated test problems. Finally, comparisons also made with how the machines break down can affect the proposed systems performance.

A hybrid project scheduling and material ordering problem: Modeling and solution algorithms

A novel combination of a multimode project scheduling problem with material ordering, in which material procurements are exposed to the total quantity discount policy is investigated in this paper. The study aims at finding an optimal Pareto frontier for a triple objective model derived for the problem. While the first objective minimizes the makespan of the project, the second objective maximizes the robustness of the project schedule and finally the third objective minimizes the total costs pertaining to renewable and nonrenewable resources involved in a project. Four well-known multi-objective evolutionary algorithms including non-dominated sorting genetic algorithm II (NSGAII), strength Pareto evolutionary algorithm II (SPEAII), multi objective particle swarm optimization (MOPSO), and multi objective evolutionary algorithm based on decomposition (MOEAD) solve the developed triple-objective problem. The parameters of algorithms are tuned by the response surface methodology. The algorithms are carried out on a set of benchmarks and are compared based on five performance metrics evaluating their efficiencies in terms of closeness to the optimal frontier, diversity, and variance of results. Finally, a statistical assessment is conducted to analyze the results obtained by the algorithms. Results show that the NSGAII considerably outperforms others in 4 out of 5 metrics and the MOPSO performs better in terms of the remaining metric.

Entransy analysis and optimization of irreversible Carnot-like heat engine

Owing to the energy demands of the world and the issues involved with global warming, analyzing and optimizing power cycles have increased in importance. In this work, the concepts of entransy dissipation, power output, entropy generation, energy, exergy output, exergy efficiencies for irreversible heat engine cycles are applied as a means of analyzing them. This paper presents thermo-dynamical study of an irreversible heat engine cycle with the aim of optimizing the performance of the heat engine cycle. Moreover, four different strategies in the process of multi-objective optimization are proposed, and the outcomes of each strategy are evaluated separately. In the first scenario, in order to maximize the exergy output, ecological coefficient of performance (ECOP) and exergy-based ecological function (EECF), a multi-objective optimization algorithm was executed. In the second scenario, three objective functions comprising the ecological function, ECOP and exergetic performance criterion were maximized at the same time by employing multi objective optimization algorithms. In the third scenario, in order to minimize the entransy dissipation and maximize the ECOP and EECF, a multi-objective optimization algorithm was executed. In the fourth scenario, three objective functions comprising the exergetic performance criterion and ECOP and EECF were maximized at the same time by employing multi objective optimization algorithms. All the strategies in the present work are executed via the multi objective evolutionary algorithms based on NSGA|| method. Finally, to govern the final answer in each strategy, three well-known decision makers are executed.

A Hybrid Pareto Based Multi Objective Evolutionary Algorithm for a Partial Flexible Open-Shop Scheduling Problem

A Hybrid Pareto Based Multi Objective Evolutionary Algorithm for a Partial Flexible Open-Shop Scheduling Problem

Clustering Embedded with Context Awareness using an Evolutionary Approach

research presented in this paper explores the embedding of context awareness into a data mining method called clustering. Adding context to traditional data mining methods has been known to improve results of information retrieval systems. The approach used for this task is that of Multi Objective Evolutionary Algorithms. Evolutionary algorithms imitate the biological process of natural selection, also known as survival of the fittest, to solve computational problems. It is a heuristic method that finds approximate solutions. The solutions are generally optimized with respect to some system objective. However, many practical problems require optimization in more than one and possibly conflicting objectives. Multi Objective Evolutionary Algorithms (MOEA) are used for this purpose.

Decomposition based Multi Objective Evolutionary Algorithms for Design of Large-Scale Water Distribution Networks

In last two decades, multiobjective evolutionary algorithms (MOEAs) have shown their merit for solving different optimization problems within the context of water resources and environmental engineering. MOEAs mainly use the concept of Pareto dominance for obtaining the trade-off solutions considering different criteria. A new alternative method for solving multiobjective problems is multiobjective evolutionary algorithm based on decomposition (MOEA/D) which uses scalarizing the objective functions. In this paper, decomposition strategies are developed for the large-scale water distribution network (WDN) design problems by integrating the concepts of harmony search (HS) and genetic algorithm (GA) within the MOEA/D framework. The proposed algorithms are then compared with two well-known non-dominance based MOEAs: NSGA2 and SPEA2 across four different WDN design problems. Experimental results show that MOEA/D outperform the Pareto dominance methods in terms of both non-domination and diversity criteria. MOEA/D-HS in particular could provide very high quality solutions with a uniform distribution along the Pareto front preserving the diversity and dominating the solutions of the other algorithms. It suggests that decomposition based multiobjective evolutionary algorithms are very promising in dealing with complicated large-scale WDN design problems.

Multi-objective metaheuristics for solving a type II robotic mixed-model assembly line balancing problem

Nowadays, robots are used extensively in robotic assembly line balancing system because of the capabilities of the robots. Robotic assembly lines are used to manufacture high volume product in customization and specialization production. In this paper, type II robotic mixed-model assembly line balancing is considered. The goals are to minimize robot purchasing costs, robot setup costs, sequence dependent setup costs, and cycle time. The proposed model tries to determine an optimal or near-optimal configuration of tasks, workstations in U-shaped assembly line balancing. In this model, two types of tasks including the special task for one product model and the common task for several products models exist. The problem with the aforementioned conditions is NP-hard problem. So, we used two different multi- objective evolutionary algorithms (MOEAs) to solve the problem. First algorithm is non-dominated sorting genetic algorithm (NSGA-II) and the second one is multi-objective particle swarm optimization (MOPSO). Also, we used GAMS software to solve the problem in small size problem to validate our proposed model. Then, some numerical examples are presented and the experimental results and the performance of the algorithms are compared with each other.

A multi‐objective optimization algorithm based on self‐organizing maps applied to wireless power transfer systems

SummaryIn this work, a new multi‐objective population‐based optimization algorithm is presented and tested. In this contribution, the concepts of fast non‐dominating sorting and density estimation using the crowding distance are used to create a multi‐objective optimization algorithm based on previous work, which is a single objective evolutionary optimization algorithm based on self‐organizing maps (SOMs). The SOMs paradigm introduces a strong collaboration between neighbors solutions that improves exploitation. Furthermore, the representative power of the SOMs enhances the exploration and diversification. A state of the art benchmark approach is used to evaluate the performance of the proposed algorithm, obtaining positive results. The test problem uses an analytical model of an inductively coupled wireless power transfer system (WPT). The objective is to optimize the WPT model characteristics in order to allow simultaneous data and power transfer between the coils. The WPT design approach uses more degrees of freedom than existing techniques leading to a number of solutions where both the power signals and the data signal can coexist on the same physical channel achieving good figures of merit. Copyright © 2016 John Wiley & Sons, Ltd.

Multi Objective Evolutionary Algorithms for Association Rule Mining: Advances and Challenges

Multi Objective Evolutionary Algorithms for Association Rule Mining: Advances and Challenges

Recognizing Surgically Altered Face Images and 3D Facial Expression Recognition

Altering Facial appearances using surgical procedures are common now days. But it raised challenges for face recognition algorithms. Plastic surgery introduces non linear variations. Because of these variations it is difficult to be modeled by the existing face recognition system. Here presents a multi objective evolutionary granular algorithm. It operates on several granules extracted from a face images at multiple level of granularity. This granular information is unified in an evolutionary manner using multi objective genetic approach. Then identify the facial expression from the face images. For that 3D facial shapes are considering here. A novel automatic feature selection method is proposed based on maximizing the average relative entropy of marginalized class-conditional feature distributions and apply it to a complete pool of candidate features composed of normalized Euclidian distances between 83 facial feature points in the 3D space. A regularized multi-class AdaBoost classification algorithm is used here to get the highest average recognition rate.

Fast Solutions Enhancing using a Copula-based EDA and SVM for many-objective problems

In this paper we propose a new Copula-based Estimation of Distribution Algorithm, to solve Many-objective optimization problem and to get new optimal solutions in very court time. Our algorithm uses the proprieties of Copula and exploits their statistical properties to make new solutions using the founded optimal solutions through the estimation of their distribution. The first step of the proposed Copula-based Estimation of Distribution Algorithm (CEDA-SVM) is taking initial solutions offered by any MOEA (Multi Objective Evolutionary Algorithm), and then creates Copulas to estimate their distribution, and we use Support Vector Machine (SVM) to learn the Pareto solutions model; those Copulas will be used to generate new solutions and SVM to avoid the expensive function evaluations. The idea of using the estimated distribution of the optimal solutions helps CEDA-SVM to avoid running the optimizer ( MOEA ) every time we need new alternatives solutions when the founded ones are not satisfactory. We tested CEDA-SVM on a set of many-objective benchmark problems traditionally used by the community, namely DTLZ (1, 2, 3, and 4) with different dimensions (3, 5, 8, 10, and 15). We used CEDA along with MOEA/D-Schy and MOEA/D-BI as two examples of MOEA thus resulting in two variants CEDA-MOAE/D-Scy and CEDA-MOEA/D-BI and compare them with MOEA/D-Schy and MOEA/D-BI. The results of our experiments show that, with both variants of CEDA-SVM, new solutions can be obtained in a very small time compared to the other algorithms.

A bi-objective model for wireless sensor deployment considering coverage and tracking applications

In recent years, wireless sensor networks WSNs have become very attractive for surveillance applications and particularly for target tracking. When a target has to be located by a WSN, accuracy is an important constraint. Most of the studies made in the WSNs problems deal with either coverage or tracking focus objectives. In this paper, we study a bi-objective sensor placement problem taking into account both coverage and accuracy. The objectives are the minimisation of the number of deployed sensors and the minimisation of the imprecision, under the coverage constraints. The non sorting genetic algorithm NSGA-II and multi objective evolutionary algorithm based on decomposition MOEA/D have been implemented to solve the problem. The performances of these algorithms are checked with integer programming results for small size instances, and they are compared on large size instances by multi-objective metrics. Results have shown that both implemented algorithms provide optimal solutions for almost small size instances. NSGA-II results are better than MOEA/D on the small size instance set, while MOEA/D outperforms NSGA-II on the large size instance set.

Thermodynamic analysis and optimization for an irreversible heat pump working on reversed Brayton cycle

This research presents the optimization and performance analysis for irreversible heat pumps operating with reversed Brayton cycle with constant-temperature heat reservoirs via finite-time thermodynamics (FTT) by considering exergetic efficiency as the optimization objective combining exergy model. Moreover, two different strategies in the process of multi-objective optimization are proposed, and the outcomes of each strategy are evaluated separately. The first strategy is proposed to maximize the coefficient of performance (COP), the ecological coefficient of performance (ECOP) and the exergetic efficiency. Furthermore, the second strategy is suggested to maximize the COP, ECOP and Ecological function. All the strategies in the present work are executed via the multi objective evolutionary algorithms based on NSGA|| method. Finally, to govern the final answer in each strategy, three efficient decision makers were executed.