Evolutionary Multi-objective Optimization Technique Research Articles

An integrative approach to protein sequence design through multiobjective optimization.

With recent methodological advances in the field of computational protein design, in particular those based on deep learning, there is an increasing need for frameworks that allow for coherent, direct integration of different models and objective functions into the generative design process. Here we demonstrate how evolutionary multiobjective optimization techniques can be adapted to provide such an approach. With the established Non-dominated Sorting Genetic Algorithm II (NSGA-II) as the optimization framework, we use AlphaFold2 and ProteinMPNN confidence metrics to define the objective space, and a mutation operator composed of ESM-1v and ProteinMPNN to rank and then redesign the least favorable positions. Using the two-state design problem of the foldswitching protein RfaH as an in-depth case study, and PapD and calmodulin as examples of higher-dimensional design problems, we show that the evolutionary multiobjective optimization approach leads to significant reduction in the bias and variance in RfaH native sequence recovery, compared to a direct application of ProteinMPNN. We suggest that this improvement is due to three factors: (i) the use of an informative mutation operator that accelerates the sequence space exploration, (ii) the parallel, iterative design process inherent to the genetic algorithm that improves upon the ProteinMPNN autoregressive sequence decoding scheme, and (iii) the explicit approximation of the Pareto front that leads to optimal design candidates representing diverse tradeoff conditions. We anticipate this approach to be readily adaptable to different models and broadly relevant for protein design tasks with complex specifications.

Hyperspectral band selection using a decomposition based multiobjective wrapper approach

Presently, sensors acquire data in a broad spectrum of contiguous bands with a high correlation in the available information among the neighboring bands. Due to the complexity of dealing with a large number of bands, selecting fewer representative spectral bands without compromising the information quantum has gained much attention. However, obtaining an optimum number of spectral bands without deteriorating the information value is a challenging problem. The present study proposes an evolutionary multiobjective optimization (EMO) based wrapper approach for optimal band selection. The efficacy of a wrapper approach depends on the performance of the underlying classifier. In this work, the simultaneous selection of optimal spectral bands and the hyperparameter of the classifier is carried out by applying a new mechanism that uses the power distribution-based interaction strategy for candidate generation. A bi-objective optimization problem is formulated with percentage reduction in bands and the corresponding classification accuracy as the objective functions. Five widely referenced hyperspectral datasets are used to assess the effectiveness of the proposed method. Experimental results demonstrate that the proposed technique selects a significantly lower number of bands while preserving information quantum thereby achieving better classification accuracy. Additionally, a comparison with other EMO techniques confirms the superior performance of the proposed method in finding a lower number of spectral bands without compromising the spectral information.

An Evolutionary Multi-objective Optimization Technique to Deploy the IoT Services in Fog-enabled Networks: An Autonomous Approach

ABSTRACT The Internet of Things (IoT) generates countless amounts of data, much of which is processed in cloud data centers. When data is transferred to the cloud over longer distances, there is a long latency in IoT services. Therefore, in order to increase the speed of service provision, resources should be placed close to the user (i.e., at the edge of the network). To address this challenge, a new paradigm called Fog Computing was introduced and added as a layer in the IoT architecture. Fog computing is a decentralized computing infrastructure in which provides storage and computing in the vicinity of IoT devices instead of sending to the cloud. Hence, fog computing can provide less latency and better Quality of Service (QoS) for real-time applications than cloud computing. In general, the theoretical foundations of fog computing have already been presented, but the problem of IoT services placement to fog nodes is still challenging and has attracted much attention from researchers. In this paper, a conceptual computing framework based on fog-cloud control middleware is proposed to optimally IoT services placement. Here, this problem is formulated as an automated planning model for managing service requests due to some limitations that take into account the heterogeneity of applications and resources. To solve the problem of IoT services placement, an automated evolutionary approach based on Particle Swarm Optimization (PSO) has been proposed with the aim of making maximize the utilization of fog resources and improving QoS. Experimental studies on a synthetic environment have been evaluated based on various metrics including services performed, waiting time, failed services, services cost, services remaining, and runtime. The results of the comparisons showed that the proposed framework based on PSO performs better than the state-of-the-art methods.

MULTI-OBJECTIVE OPTIMIZATION OF LIQUID COOLING SYSTEM FOR A TWELVE-CELL BATTERY MODULE

In this research, two cooling plates with six parallel channels are designed for a twelve-cell battery module. The heat generated by a Li-ion battery cell is numerically modeled, and the numerical model is validated with the experimental data. The temperature difference of the battery cells in the battery module is an important factor for the capacity usage and cycle life of a battery module. The aim of this study is to design an optimum cooling system that will increase the cycle life of the batteries by decreasing the temperature difference and reducing the parasitic power consumption of the pump by reducing the pressure drop. The channel height, channel width, and the ratio of the outlet height to the inlet height are selected as design variables. In recent years, several evolutionary multi-objective optimization techniques have been presented to improve the performance of thermal management systems. In this study, CMOPSO is used for the optimization of the liquid cooling system. The results of the NSGA-II, NSGA-III, MOPSO, and CMOPSO techniques are evaluated to compare the efficiency of different optimization techniques. The results of four different multi-objective optimization methods are close to each other and have good agreement with the CFD results to reduce the temperature difference and pressure drop. A 30.3% decrease in the temperature difference and a 5.3% decrease in the total pressure drop are achieved with CMOPSO as the optimization technique. The results show the effectiveness of CMOPSO as the optimization technique for the design of battery cooling systems.

Evolutionary multi-objective automatic clustering enhanced with quality metrics and ensemble strategy

Automatic clustering problem, which needs to detect the appropriate clustering without a pre-defined number of clusters (k), is difficult and challenging in unsupervised learning owing to the lack of prior domain knowledge. Despite a rising tendency with the application of evolutionary multi-objective optimization (EMO) techniques for automatic clustering, there still exist some obvious under-explored issues. In this paper, we resort to quality metrics and ensemble strategy for the sake of explicit/implicit knowledge discovery to guide the optimization process. The quality and diversity of solutions defined in terms of cluster validities, as similar to performance indicator for multi-objective optimization, are applied to assist in addressing automatic clustering problems and decreasing unnecessary computational overhead. To be specific, the main components like initialization, reproduction operations, and environmental selection which involved during EMO based automatic clustering are discussed and refined. For the determination of the final partitioning, quality metrics and cluster ensemble strategy are both considered to improve the retrieve system in the unsupervised way. Experiments are conducted from several different aspects and the corresponding analyses are provided, which confirm that the proposals are more efficient and effective for automatic clustering.

Smart-home electrical energy scheduling system using multi-objective ant lion optimizer and evidential reasoning

Smart-home energy-management-systems (SHEMSs) are widely used for energy management in smart buildings. Energy management in smart homes is an arduous task and necessitates efficient scheduling of appliances in buildings. Scheduling of smart appliances is usually enmeshed by various and sometimes contradictory criteria which should be considered concurrently in the scheduling process. Multi-criteria decision-making (MCDM) techniques are able to select the most suitable alternative among copious ones. This paper tailors a comprehensive framework which merges MCDM techniques with evolutionary multi-objective optimization (EMOO) techniques for selecting the most proper schedule for appliances by creating a trade-off between optimization criteria. A Multi-Objective Ant Lion Optimizer (MOALO) is tailored and tested on a smart home case study to detect all the Pareto solutions. A benchmark instance of the appliance scheduling is solved employing the proposed methodology, Shannon’s entropy technique is employed to find the objectives’ corresponding weights, and afterward, the acquired Pareto optimal solutions are ranked utilizing the Evidential Reasoning (ER) method. By inspecting the efficiency of every solution considering multiple criteria such as unsafety, electricity cost, delay, Peak Average Ratio, and CO2 emission, the proposed approach confirms its effectiveness in enhancing the method for smart appliance scheduling.

Finding a Pareto Optimal Solution for a Multi-Objective Problem of Managing Radio Resources in LTE-A Systems: A QoS Aware Algorithm

Due to ever increasing users demands, fulfilling the needs of growing users is challenging with constrained resources for Long Term Evolution- Advanced (LTE-A). In this research paper we have proposed an innovative approach for Radio Resource Management (RRM) that makes use of the evolutionary multi-objective optimization techniques for Quality of Service (QoS) facilitation and embeds it with the modern techniques for RRM. We have proposed a novel algorithm that selects an optimal solution out of a set of a representative Pareto front in accordance with the users QoS requirements. It is completely novel as it uses the multi-objective optimization algorithms for achieving desired QoS in LTE-A system. The efficiency and performance of the proposed work has been further improved by autonomously assigning and managing resources among various users and applications. The results have been discussed that showcase efficient system performance and better user experience.

An Efficient Hybrid Approach of Finite Element Method, Artificial Neural Network-Based Multiobjective Genetic Algorithm for Computational Optimization of a Linear Compliant Mechanism of Nanoindentation Tester

This paper proposes a new evolutionary multiobjective optimization technique for a linear compliant mechanism of nanoindentation tester. The mechanism design is inspired by the elastic deformation of flexure hinge. To improve overall static performances, a multiobjective optimization design was carried out. An efficient hybrid optimization approach of central composite design (CDD), finite element method (FEM), artificial neural network (ANN), and multiobjective genetic algorithm (MOGA) is developed to solve the optimization problem. In this procedure, the CDD is used to lay out the experimental data. The FEM is developed to retrieve the quality performances. And then, the ANN is developed as black box to call the pseudo-objective functions. Unlike previous studies on multiobjective evolutionary algorithms, most of which generating only one Pareto-optimal solution, this proposed approach can generate more than three Pareto-optimal solutions. Based on the user’s real-work problem, one of the best optimal solutions is chosen. The results showed that the optimal results were found at the displacement of 330.68 μm, stress of 140.65 MPa, and safety factor of 3.6. The statistical analysis is conducted to investigate the behavior of the MOGA. The sensitivity analysis was carried out to determine the significant contribution of each factor. The results revealed that the lengths and thickness almost significantly affect both responses. It confirms that the proposed hybrid optimization approach gains high robustness and effectiveness with flexible decision maker rules to solve complex optimization engineering problems.

Evolutionary multi-objective optimization for bulldozer and its blade in soil cutting

ABSTRACTThe paper targets an optimal soil cutting operation by considering economic and productive aspects. Three realistic objectives and three problem-specific constraints are developed, and the optimization problem is solved using a hybrid evolutionary multi-objective optimization (EMO) technique. In this technique, a set of non-dominated solutions is generated by using an existing EMO technique, and then a few of them are selected for local search using the -constraint method. These selected solutions are used for starting independent local searches using fmincon solver of Matlab. Results demonstrate that the local searches have improved the non-dominated solutions a little, thereby suggesting a closeness of evolved solutions from EMO technique with true Pareto-optimal (PO) solutions. The PO solutions are further validated using experimental data from the literature. Overall, this study offers a platform to choose an appropriate solution from the set of PO solutions. Moreover, the post-optimal analysis demonstrates the commonality principle of few decision variables, which is followed by all PO solutions. The rest of the decision variables decipher important relationships that are responsible for trade-off among the PO solutions. The relationships are later used for preparing guidelines for a practitioner in selecting an appropriate solution for the optimal operation.

Optimal Location and Sizing of Distributed Generation Unit Using Human Opinion Dynamics Optimization Technique

The demand of electricity is soaring rapidly. Distributed genera-tion (DG) is one of the most suitable alternatives to fulfill this swellingdemand of energy. DG is a small scale generation which is directly in-stalled in the distribution network or at load centre. Optimal allocationof DG is a vital factor in improving the voltage profile of the system andin reduction of total power losses. In this article, a detailed study of threedifferent methods for DG allocation and sizing has been discussed. Thefirst method is based on Newton Raphson load flow based technique todeduce the optimal location of DG in two different IEEE bus systems inMATLAB software. The next methodology is based on particle swarmoptimization (PSO) technique where a multi-objective function is beingminimized. The objective function has been modified and PSO has beenimplemented to attain optimal size and location of DG unit. The thirdmethod considered is based on human opinion dynamics evolution-ary multi-objective optimization technique which is used to obtain thebest possible size and location of DG unit in IEEE 14 and IEEE 30 bussystems. The human opinion dynamics method shows superiority inminimizing the size and location muti-objective function, over the othermethods considered herein.

Optimal Location and Sizing of Distributed Generation Unit Using Human Opinion Dynamics Optimization Technique

The demand of electricity is soaring rapidly. Distributed generation (DG) is one of the most suitable alternatives to fulfill this swelling demand of energy. DG is a small scale generation which is directly installed in the distribution network or at load centre. Optimal allocation of DG is a vital factor in improving the voltage profile of the system and in reduction of total power losses. In this article, a detailed study of three different methods for DG allocation and sizing has been discussed. The first method is based on Newton Raphson load flow based technique to deduce the optimal location of DG in two different IEEE bus systems in MATLAB software. The next methodology is based on particle swarm optimization (PSO) technique where a multi-objective function is being minimized. The objective function has been modified and PSO has been implemented to attain optimal size and location of DG unit. The third method considered is based on human opinion dynamics evolutionary multi-objective optimization technique which is used to obtain the best possible size and location of DG unit in IEEE 14 and IEEE 30 bus systems. The human opinion dynamics method shows superiority in minimizing the size and location muti-objective function, over the other methods considered herein.

Evolutionary Multiobjective Optimization-Based Multimodal Optimization: Fitness Landscape Approximation and Peak Detection

Recently, by taking advantage of evolutionary multiobjective optimization techniques in diversity preservation, the means of multiobjectivization has attracted increasing interest in the studies of multimodal optimization (MMO). While most existing work of multiobjectivization aims to find all optimal solutions simultaneously, in this paper, we propose to approximate multimodal fitness landscapes via multiobjectivization, thus providing an estimation of potential optimal areas. To begin with, an MMO problem is transformed into a multiobjective optimization problem (MOP) by adding an adaptive diversity indicator as the second optimization objective, and an approximate fitness landscape is obtained via optimization of the transformed MOP using a multiobjective evolutionary algorithm. Then, on the basis of the approximate fitness landscape, an adaptive peak detection method is proposed to find peaks where optimal solutions may exist. Finally, local search is performed inside the detected peaks on the approximate fitness landscape. To assess the performance of the proposed algorithm, extensive experiments are conducted on 20 multimodal test functions, in comparison with three state-of-the-art algorithms for MMO. Experimental results demonstrate that the proposed algorithm not only shows promising performance in benchmark comparisons, but also has good potential in assisting preference-based decision-making in MMO.

An Artificial Intelligence Approach for the Stochastic Management of Coastal Aquifers

Aquifer recharge rates and patterns are often uncertain, especially in arid areas due to sporadic and erratic rainfall. Therefore, determining the optimal groundwater abstraction using classical approaches such as Monte Carlo Simulation (MCS) requires a large number of groundwater simulations and exorbitant computational efforts. The problem becomes even more complex and time consuming for regional coastal aquifers whose domains must be discretized using high-resolution meshes. In fact, even fast evolutionary multi-objective optimization techniques generally require a large number of simulations to determine the Pareto-front among the objectives. This study explores the performance of a Decision Tree (DT) approach for the generation of the Pareto optimal solutions of groundwater extraction. This paper applies the DTs for the optimal management of the Al-Khoud coastal aquifer in Oman. The learning process of the developed DT-based model uses the output of a numerical simulation model to assess the aquifer response based on different abstraction policies. The trained DT network then utilizes the NSGA-II to determine the Pareto-optimal solutions. The simulation show that the general flux pattern in the study area is toward the sea and the hydraulic head following a similar pattern in both best and worst recharging scenarios downstream of the studied recharging dam. Statistical tests showed a good correlation between the DT-based and simulation-based results and demonstrate the capability of the DT approach to obtain high-quality solutions by incorporating a large number of recharge scenarios. Moreover, the required runtime of the DT-based approach is extremely low (5 min) compared to that of the simulation-based method (several days). This means that including additional Monte-Carlo simulations can be readily done in few minutes using the obtained DTs, instead of the long computational time needed by the simulation-based approach.

Multi-Objective Model Selection for Unmanned Aerial Vehicles Automatic Target Recognition Systems

Within unmanned aerial vehicles on-going research topics, automatic target recognition is acquiring relevance. This is due to the easiness with which it is possible to acquire such devices. In order to do this, signal processing and classification techniques could be adopted. Also, in order to improve the classification ratio, optimization techniques could be used. This subject is object of study for many researchers, but the authors worry about the lack of information while using single-objective optimization techniques. The proposed work comprises the application of multi-objective optimization design in order to create an automatic target recognition system to discriminate different types of unmanned aerial vehicles. In order to accomplish this, a K-band radar system is used to send and receive electromagnetic signals, which are then processed with feature extraction techniques, and finally, applied on an artificial neural network system. In order to improve the system’s classification ratio, the classifier is defined as a multi-objective problem, and evolutionary multi-objective optimization techniques are applied. Finally, in order to select the best possible trade-off, the level diagrams multi-criteria decision making methodology is used to compare different solutions.

QoS Oriented Multiobjective Optimizer for Radio Resource Management of LTE-A Femtocells

The future wireless networks support multimedia applications and require ensuring quality of the services they provide. With increasing number of users, the radio resource is becoming scarce. Therefore, how should the demands for higher data rates with limited resources be met for Long Term Evolution-Advanced (LTE-A) is turning out to be a vital issue. In this research paper we have proposed an innovative approach for Radio Resource Management (RRM) that makes use of the evolutionary multiobjective optimization (MOO) technique for Quality of Service (QoS) facilitation and embeds it with the modern techniques for RRM. We have proposed a novel Multiobjective Optimizer (MOZ) that selects an optimal solution out of a Pareto optimal (PO) set in accordance with the users QoS requirements. We then elaborate the scheduling process and prove through performance evaluation that use of MOO can provide potential solutions for solving the problems for resource allocation in the advancement of LTE-A networks. Simulations are carried out using LTE-Sim simulator, and the results reveal that MOZ outperforms the reference algorithm in terms of throughput guarantees, delay bounds, and reduced packet loss. Additionally, it is capable of achieving higher throughput and lower delay by giving equal transmission opportunity to all users and achieves 100% accuracy in terms of selecting optimal solution.

Multi-Objective Optimal Design of a Double Circular Gear Based on the Differential Evolution Algorithm

In order to improve the comprehensive technical and economic indicators of a double circular gear, based on the conjugate principle and design method of the double circular gear, by use of the modified differential evolution multi-objective optimization technique and MATLAB computer simulation technology, constrained multi-objective optimization design of a double circular gear was done. According to the research process and results, by use of the improved differential evolutionary multi-objective optimization technique, the design cycle of product can be shorten effectively, the design quality of product can be improved.

Integrated ANN-HMH Approach for Nonlinear Time-Cost Tradeoff Problem

AbstractThis paper presents an integrated Artificial Neural Network - Hybrid Meta Heuristic(ANN-HMH) method to solve the nonlinear time-cost tradeoff(TCT) problem of real life engineering projects. ANN models help to capture the existing nonlinear time-cost relationship in project activities. ANN models are then integrated with HMH technique to search for optimal TCT profile. HMH is a proven evolutionary multiobjective optimization technique for solving TCT problems. The study has implication in real time monitoring and control of project scheduling processes.

Multi-objective optimization using NSGA-II for power distribution system reconfiguration

SUMMARY This study proposes a new strategy to solve the problem of radial power distribution system (RDS) reconfiguration in a multi-objective and constrained environment. Due to the presence of various conflicting objectives and constraints, the proposed strategy uses the Elitist Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), an effective evolutionary multi-objective optimization technique. NSGA-II determines a set of pareto-optimal solutions for the power distribution system topology, considering power losses, reliability and investment in tie-switches. The methodology adopted to evaluate the RDS reliability uses a non-sequential Monte Carlo Simulation and is focused on the impacts of branch failures for interruption energy assessment. The effectiveness of the proposed methodology is demonstrated on a 69 bus RDS. Copyright © 2013 John Wiley & Sons, Ltd.

Robustness Against the Decision-Maker's Attitude to Risk in Problems With Conflicting Objectives

In multiobjective optimization problems (MOPs), the Pareto set consists of efficient solutions that represent the best trade-offs between the conflicting objectives. Many forms of uncertainty affect the MOP, including uncertainty in the decision variables, parameters or objectives. A source of uncertainty that is not studied in the evolutionary multiobjective optimization (EMO) literature is the decision-maker's attitude to risk (DMAR) even though it has great significance in real-world applications. Often the decision-makers change over the course of the decision-making process and thus, some relevant information about preferences of future decision-makers is unknown at the time a decision is made. This poses a major risk to organizations because a new decision-maker may simply reject a decision that has been made previously. When an EMO technique attempts to generate the set of nondominated solutions for a problem, then DMAR-related uncertainty needs to be reduced. Solutions generated by an EMO technique should be robust against perturbations caused by the DMAR. In this paper, we focus on the DMAR as a source of uncertainty and present two new types of robustness in MOP. In the first type, dominance robustness (DR), the robust Pareto solutions are those which, if perturbed, would have a high chance to move to another Pareto solution. In the second type, preference robustness (PR), the robust Pareto solutions are those that are close to each other in configuration space. Dominance robustness captures the ability of a solution to move along the Pareto optimal front under some perturbative variation in the decision space, while PR captures the ability of a solution to produce a smooth transition (in the decision variable space) to its neighbors (defined in the objective space). We propose methods to quantify these robustness concepts, modify existing EMO techniques to capture robustness against the DMAR, and present test problems to examine both DR and PR.

A software framework based on a conceptual unified model for evolutionary multiobjective optimization: ParadisEO-MOEO

This paper presents a general-purpose software framework dedicated to the design and the implementation of evolutionary multiobjective optimization techniques: ParadisEO-MOEO. A concise overview of evolutionary algorithms for multiobjective optimization is given. A substantial number of methods has been proposed so far, and an attempt of conceptually unifying existing approaches is presented here. Based on a fine-grained decomposition and following the main issues of fitness assignment, diversity preservation and elitism, a conceptual model is proposed and is validated by regarding a number of state-of-the-art algorithms as simple variants of the same structure. This model is then incorporated into the ParadisEO-MOEO software framework. This framework has proven its validity and high flexibility by enabling the resolution of many academic, real-world and hard multiobjective optimization problems.