Strength Pareto Evolutionary Algorithm II Research Articles

Multi-objective optimization based robotic path planning for interpolation reconstruction model of CT data

Robotic path planning plays a pivotal role in computer-aided liver tumor thermal ablation surgery. However, traditional methods face challenges in terms of low reconstruction efficiency and planning safety. To address these issues, we propose a method for robotic path planning of liver tumor thermal ablation surgery. Firstly, an interlayer interpolation algorithm based on optical flow estimation is utilized to compensate for large interlayer spacing in computed tomography (CT) images by inserting predicted images between sequence images. Secondly, the voxel traversal strategy and patch intersection calculation strategy in the standard marching cube (MC) algorithm is optimized to improve the efficiency of abdominal tissues reconstruction. Finally, comprehensive clinical constraints are summarized to ensure the surgery safety and the strength pareto evolutionary algorithm II (SPEA-II) is leveraged to optimize surgery path, which can obtain even distribution of solutions in high-dimension optimization problems. Extensive experiments conducted on the 3Dircadb and SLIVER07 datasets revealed that our proposed method reduces reconstruction time by 21.5% compared to the standard MC algorithm, while achieving an average overlap rate of 88.25% and an average Hausdorff distance of 15.25 mm between Pareto front points and surgeon's recommended puncture points.

Multi-objective optimization of micro-gas turbine coupled with LCPV/T combined cooling, heating and power (CCHP) system based on following electric load strategy

A distributed energy system integrated with solar and natural gas can achieve cascade energy utilization, high efficiency and energy-saving, and significantly reduce carbon emissions, which is receiving increasing attention. Therefore, this paper establishes an optimization model for primary energy consumption saving rate (PECSR), annual total cost saving rate (ATCSR), carbon dioxide emission reduction rate (CDERR) and exergy efficiency of micro-gas turbine coupled with low-concentrating photovoltaic/thermal (LCPV/T) CCHP system, and analyzes the influence of the parameters, such as solar irradiation, LCPV/T inlet temperature, LCPV/T inlet flow, LCPV/T area, low-temperature water source heat pump capacity, high-temperature water source heat pump capacity, the heat transfer efficiency of waste heat recovery boiler (WHRB) and gas boiler capacity, on the objective functions. The analysis results indicate that when the heat exchange efficiency of WHRB reaches 0.71, the CCHP system is most economical. Based on this, the Strength Pareto Evolutionary Algorithm-II (SPEA-II) optimization algorithm and decision-making method combining Technique for order preference by similarity to an ideal solution (TOPSIS) and weight entropy method are employed to solve the model comprehensively, and acquire the optimum solution. After multi-objective optimization and selection, the system exergy efficiency remains around 25 %. Compared with the separation production system, the PECSR and CDERR of the CCHP system are improved by more than 24 % and 41 %, respectively.

Application of Strength Pareto Evolutionary Algorithm II in Multi-Objective Water Supply Optimization Model Design for Mountainous Complex Terrain

Water distribution networks (WDN) model optimization is an important part of smart water systems to achieve optimal strategies. WDN optimization focuses on the nonlinearity of the discharge head loss equation, the availability of discrete properties of pipe sizes, and the conservation of constraints. Multi-objective evolutionary algorithms (MOEAs) have been proposed and successfully applied in the field of WDN design optimization. Previous studies have focused on comparing the optimization effects of algorithms in water distribution networks, ignoring the problems of unbalanced pressure distribution and water hammer at the nodes of the pipe network caused by the complex terrain in mountainous areas. In this paper, a multi-objective water supply optimization model that integrated cost, reliability, and water quality was established for a mountainous WDN in real engineering. The method of traversing the nodes to solve the water age was introduced to find a more scientific and practical water age solution model, with setting the weight function to evaluate the water age of the water supply model comprehensively. Strength Pareto Evolutionary Algorithm II (SPEA-II) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) were adopted to optimize the WDN design model in the mountainous complex terrain. The significance levels of the number of Pareto solutions (NOPS) and running time are 0.029 and 0.001, respectively, indicating that the two algorithms have significant differences. Compared to NSGA-II, SPEA-II has a better convergence rate and running time in multi-objective water supply optimization design. The solution set distribution of SPEA-II is more concentrated than that of NSGA-II, also the numerical value is better. The number of SPEA-II optimization schemes is larger and the scheme is more effective. Among them, the Pareto solution set of SPEA-II can obtain more desirable optimization results on cost, reliability index (RI) and water age. In summary, the study provides valuable information for decision makers in WDN with complex terrain.

Network-based structure optimization method of the anti-aircraft system

The anti-aircraft system plays an irreplaceable role in modern combat. An anti-aircraft system consists of various types of functional entities interacting to destroy the hostile aircraft moving in high speed. The connecting structure of combat entities in it is of great importance for supporting the normal process of the system. In this paper, we explore the optimizing strategy of the structure of the anti-aircraft network by establishing extra communication channels between the combat entities. Firstly, the thought of combat network model (CNM) is borrowed to model the anti-aircraft system as a heterogeneous network. Secondly, the optimization objectives are determined as the survivability and the accuracy of the system. To specify these objectives, the information chain and accuracy chain are constructed based on CNM. The causal strength (CAST) logic and influence network (IN) are introduced to illustrate the establishment of the accuracy chain. Thirdly, the optimization constraints are discussed and set in three aspects: time, connection feasibility and budget. The time constraint network (TCN) is introduced to construct the timing chain and help to detect the timing consistency. Then, the process of the multi-objective optimization of the structure of the anti-aircraft system is designed. Finally, a simulation is conducted to prove the effectiveness and feasibility of the proposed method. Non-dominated sorting based genetic algorithm-II (NSGA2) is used to solve the multi-objective optimization problem and two other algorithms including non-dominated sorting based genetic algorithm-III (NSGA3) and strength Pareto evolutionary algorithm-II (SPEA2) are employed as comparisons. The deciders and system builders can make the anti-aircraft system improved in the survivability and accuracy in the combat reality.

A Hybrid and Light Weight Metaheuristic Approach with Clustering for Multi-Objective Resource Scheduling and Application Placement in Fog Environment

Fog computing is receiving considerable attention in the research community to deliver computing resources for Internet of Things (IoT) devices. With rapid advancements in IoT, it is necessary to cater to the applications closer to the user using fog which acts as a potential complement to the cloud. In practice, scheduling of fog devices and allocation of IoT applications to fog play a key role, if improperly done, IoT applications in a fog environment may not be deployed effectively. As a result, there is a wastage of resources, an increase in network latency, and a burden on Quality of Service (QoS) on the fog nodes, resulting in a multi-objective optimization problem. To analyze the scheduling and allocation problem of fog nodes this work proposes a combinatorial novel model based on a metaheuristic approach called Enhanced Multi-Objective Particle Swarm Optimization with Clustering (EMOPSOC) and Fog Picker. The proposed EMOPSOC is alightweight algorithmthat dynamically schedules the fog nodes based on a multi-objective optimization problem. The model usesthe application of machine learning techniques to enhance the evolutionary computation algorithmby minimizing the latency between the IoT and fog devices, resource wastage, and load imbalances, as well as making efficient use of the resources and reducing turnaround time. In addition, the Fog Picker deployed in the model allocates the IoT components to the fog nodes based on QoS attributes. The proposed approach is simulated and tested using iFogSim and jMetal. The performance of the proposed algorithm was tested on several benchmark test functions using Inverted Generational Distance (IGD) and Hypervolume (HV) indicators. The experimental results show that EMOPSOC in combination with Fog Picker has outperformed the variants of Multi-Objective Particle Swarm Optimization (MOPSO) algorithms and state-of-the-art algorithms such as Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), Strength Pareto Evolutionary Algorithm-II (SPEA-II) by maintaining meritoriously distributed populations, converging quickly, and maintaining steady performance without forfeiting steadiness. Finally, the numerical results verify, that the optimal solution set has improved convergence and divergence over the relative methods. The experimental results of the Fog Picker have increased by 80%, indicating that the efficiency is greatly improved even when the fog nodes and IoT application components are increased.

A sustainable-circular citrus closed-loop supply chain configuration: Pareto-based algorithms

Configuration of sustainable supply chains for agricultural products has been a well-known research field recently which is continuing to evolve and grow. It is a complex network design problem, and despite the abundant literature in the field, there are still few models offered to integrate social impacts and environmental effects to support network design decision-making to support the configuration of the citrus supply chain. In this work, the citrus supply chain design problem is investigated by integrating the production, distribution, inventory control, recycling and locational decisions in which the triple bottom lines of sustainability, as well as circularity strategy, are addressed. Accordingly, a novel multi-objective Mixed-Integer Linear Programming (MILP) model is proposed to formulate a multi-period multi-echelon problem to design the sustainable citrus Closed-Loop Supply Chain (CLSC) network. To solve the developed model, the ε-constraint approach is employed in small-sized problems. Furthermore, Strength Pareto Evolutionary Algorithm II (SPEA-II) and Pareto Envelope-based Selection Algorithm II (PESA-II) algorithms are used in medium- and large-sized problems. Taguchi design technique is then utilized to adjust the parameters of the algorithms efficiently. Three well-known assessment metrics and convergence analysis are regarded to test the efficiency of the suggested algorithms. The numerical results demonstrate that the SPEA-II algorithm has a superior efficiency over PESA-II. Moreover, to validate the applicability of the developed methodology, a real case study in Mazandaran/Iran is investigated with the help of a set of sensitivity analyses.

A multi-objective decomposition evolutionary algorithm based on the double-faced mirror boundary for a milk-run material feeding scheduling optimization problem

With the development of personalized and differentiated customization demands, mixed-model assembly lines have been increasingly adopted by manufacturing enterprises due to their low-cost and high-efficiency. However, this mode has brought great challenges to the material-feeding process which is highly coupled with the production of automobile assembly lines. This paper, therefore, proposes a milk-run material-feeding scheduling problem based on two-level logistics network for modern mixed-model assembly lines, focusing on the milk-run material-feeding process between the central warehouse and line-integrated supermarkets for the first time. An integer programming mathematical model is established with the objective of simultaneously minimizing the number of electric vehicles put into use and the maximum travelling distance of the electric vehicles. Then, a double-faced mirror boundary-based multi-objective decomposition evolutionary algorithm (MOEA/D-DFMB) is provided to solve the problem. The introduction of the double-faced mirror boundary theory helps overcome the uneven distribution of Pareto solutions. Besides, the adaptive crossover operator based on evolutionary potential judgment, the adaptive adjustment strategy of mutation probability, and the local search optimization operator are applied to the proposed algorithm, so as to enhance the search ability and the convergence speed. Finally, computational experiments of the proposed algorithm are carried out, and the feasibility and effectiveness of the proposed algorithm are evaluated by comparing with the non-dominated sorting genetic algorithm-II (NSGA-II), the strength Pareto evolutionary algorithm-II (SPEA-II), the multi-objective decomposition evolutionary algorithm based on differential evolution (MOEA/D-DE), the multi-objective red deer algorithm (MORD), and the multi-objective social engineering algorithm (MOSE). The results indicate that the proposed MOEA/D-DFMB outperforms the comparison algorithms both in solution quality and convergence rate when solving the milk-run material-feeding scheduling problem.

Application of MOMSA algorithm for optimal operation of Karun multi objective multi reservoir dams with the aim of increasing the energy generation

Multi-reservoir system operation is a challenging and complex task, because of the curse of uncertainties, nonlinearities, dimensionalities and conflicts among the various contradictory objectives. Contemporaneous optimization of such competing objectives needs improving the capability of existing optimization algorithms. This study reports the first application of the recently-introduced multi-objective moth swarm algorithm (MOMSA) in optimization of long-term operation of the large Karun multi-purpose multi-reservoir system. In this algorithm, the decision variables were defined in such a way that the algorithm synchronization capability has improved. Furthermore, a robust mechanism (crowding distance mechanism) with high exploratory capability was employed to select the most efficient solutions within the population. To evaluate the algorithm's efficiency, the results of MOMSA were compared with two well-known multi-objective algorithms of the non-dominated sorting genetic algorithm-II (NSGA-II) and the strength Pareto evolutionary algorithm-II (SPEA-II). To compare the capability of these algorithms, a set of statistical indices including the generational distance (GD), the spacing (S), the spread (Δ), and the maximum spread (MS), along with four reservoir's performance evaluation indicators of the reliability (Rel), the vulnerability (Vul), the resiliency (Res), and the sustainability index were employed. The results demonstrated the superior performance of the MOMSA in solving the Karun multi-reservoir system problem with GD = 259.64, S = 3574.69, Δ = 0.755 and MS = 18420.8. The MOMSA, with the optimized average annual energy production of 15669.7 GW, outperformed the NSGA-II (7569.1 GW) and SPEA-II (9026.3 GW) algorithms. Furthermore, the developed algorithm could supply the downstream demands with a sustainability index of 86.73%, while the corresponding values for the NSGA-II, the SPEA-II and the real operating conditions were 22.73%, 46.09% and 78.87%, respectively. In addition, among the utilized algorithms, the MOMSA demonstrated the best results for flood control purpose. The superior performance of MOMSA will ensure it as a robust algorithm in the optimization of complex and large-scale problems.

A Novel Feature Selection Algorithm using Multi-Objective Improved Honey Badger Algorithm and Strength Pareto Evolutionary Algorithm-II

An important task for classification is feature selection that removes the redundant or irrelevant features from the dataset. Multi-objective feature selection approach is mainly proposed by many researchers. However, these approaches failed to maintain the higher classification accuracy while removing redundancy in the features. In this work, a wrapper based feature selection technique is proposed with a hybrid of Multi Objective Honey Badger Algorithm (MO-HBA) and Strength Pareto Evolutionary Algorithm-II to maintain the balance between classification accuracy and removal of redundancy. Classification accuracy improvement and removal of redundant features are considered as the multi-objective optimization functions of the proposed multi-objective feature selection technique. The Levy flight algorithm is utilized to initialize the population to enhance the ability of the exploration and exploitation of MO-HBA. The regularized Extreme Learning Machine is used to classify the selected features. To evaluate the performance of the proposed feature selection technique, eighteen benchmark datasets are utilized and results are compared with the four well known multi-objective feature selection techniques in terms of accuracy, hamming loss, ranking loss, mean value, standard deviation, length of features, and training time. The proposed approach achieved maximum accuracy of 100% with the maximum value of selected features as 80. The minimum value of hamming loss, ranking loss, mean value and standard deviation value achieved by the proposed approach are 0.0092, 0.0003, 0.018 and 0.001 respectively. The experimental results show that the proposed approach can give improved classification accuracy while the removal of redundancy in large scale datasets.

Multi‐objective optimization of mechanically stabilized earth retaining wall using evolutionary algorithms

AbstractThis paper uses evolutionary optimization algorithms to study the multi‐objective optimization of mechanically stabilized earth (MSE) retaining walls. Five multi‐objective optimization algorithms, including the non‐dominated sorting genetic algorithm II (NSGA‐II), strength Pareto evolutionary algorithm II (SPEA‐II), multi‐objective particle swarm optimization (MOPSO), multi‐objective multi‐verse optimization (MVO), and Pareto envelope‐based selection algorithm II (PESA‐II), are applied to the design procedure. MSE wall design requires two major requirements: external stability and internal stability. In this study, the optimality criterion is to minimize cost and its trade‐off with the factor of safety (FOS). To this end, two objectives are defined: (1) minimum cost, (2) maximum FOS. Three different strategies are considered for reinforcement combinations in the numerical simulations. Moreover, a sensitivity analysis was conducted on the variation of significant parameters, including backfill slope, wall height, horizontal earthquake coefficient, and surcharge load. The efficiency of the utilized algorithms was assessed through three well‐known coverage set measures, diversity, and hypervolume. These measures were further examined using basic statistical measures (i.e., min, max, standard deviation) and the Friedman test with a 95% confidence level.

Medical Image Captioning Using Optimized Deep Learning Model.

Medical image captioning provides the visual information of medical images in the form of natural language. It requires an efficient approach to understand and evaluate the similarity between visual and textual elements and to generate a sequence of output words. A novel show, attend, and tell model (ATM) is implemented, which considers a visual attention approach using an encoder-decoder model. But the show, attend, and tell model is sensitive to its initial parameters. Therefore, a Strength Pareto Evolutionary Algorithm-II (SPEA-II) is utilized to optimize the initial parameters of the ATM. Finally, experiments are considered using the benchmark data sets and competitive medical image captioning techniques. Performance analysis shows that the SPEA-II-based ATM performs significantly better as compared to the existing models.

Multiobjective forensic-based investigation algorithm for solving structural design problems

A multi-objective forensic-based investigation (MOFBI) algorithm is developed to solve engineering optimization problems with multiple objectives. In the proposed algorithm, a chaotic map is used to initialize the population; Lévy flight, two elite populations, and a fixed-size archive are used to operate the motions of investigators and police officers in the criminal search and arrest procedures, and a control time mechanism is used to balance exploration and exploitation in MOFBI to obtain Pareto-optimal solutions in multi-objective search spaces. Eight well-known multiple-objective metaheuristic optimization algorithms - the multi-objective ant lion optimizer (MOALO), the multi-objective dragonfly algorithm (MODA), the multi-objective evolutionary algorithm based on decomposition (MOEA/D), the multi-objective grey wolf optimizer (MOGWO), the multi-objective particle swarm optimization (MOPSO), the fast and elitist multi-objective genetic algorithm (NSGA-II), pareto envelope-based selection algorithm II (PESA-II) and the strength pareto evolutionary algorithm II (SPEA-II) - are compared to MOFBI with respect to performance in solving 24 multi-objective mathematical benchmark problems (CEC-2020 functions). The Wilcoxon rank sum test of hypervolume index, generational distance and spacing reveal that MOFBI can find more accurate approximations of Pareto-optimal fronts than the other algorithms. MOFBI is then used to solve five structural engineering design problems, including the 10-bar truss, the 72-bar truss, the 56-bar dome, the 120-bar dome and the 582-bar tower. The results obtained using MOFBI and comparison thereof with previously obtained results indicate the effectiveness of MOFBI in finding the best Pareto-optimal solutions. Therefore, MOFBI is a powerful computer-aided tool for solving multi-objective optimization problems.

A stochastic bi-objective simulation–optimization model for plasma supply chain in case of COVID-19 outbreak

As of March 24, 2020, the Food and Drug Administration (FDA) authorized to bleed the newly recovered from Coronavirus Disease 2019 (COVID-19), i.e., the ones whose lives were at risk, separate Plasma from their blood and inject it to COVID-19 patients. In many cases, as observed the plasma antibodies have cured the disease. Therefore, a four-echelon supply chain has been designed in this study to locate the blood collection centers, to find out how the collection centers are allocated to the temporary or permanent plasma-processing facilities, how the temporary facilities are allocated to the permanent ones, along with determining the allocation of the temporary and permanent facilities to hospitals. A simulation approach has been employed to investigate the structure of COVID-19 outbreak and to simulate the quantity of plasma demand. The proposed bi-objective model has been solved in small and medium scales using ɛ-constraint method, Strength Pareto Evolutionary Algorithm II (SPEA-II), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Grey Wolf Optimizer (MOGWO) and Multi Objective Invasive Weed Optimization algorithm (MOIWO) approaches. One of the novelties of this research is to study the system dynamic structure of COVID-19’s prevalence so that to estimate the required plasma level by simulation. Besides, this paper has focused on blood substitutability which is becoming increasingly important for timely access to blood. Due to shorter computational time and higher solution quality, MOIWO is selected to solve the proposed model for a large-scale case study in Iran. The achieved results indicated that as the plasma demand increases, the amount of total system costs and flow time rise, too. The proposed simulation model has also been able to calculate the required plasma demand with 95% confidence interval.

Performance comparison of multi-objective evolutionary algorithms for exergetic and exergoenvironomic optimization of a benchmark combined heat and power system

This paper compares the Non-dominated sorting genetic algorithm-II, Pareto envelope-based selection algorithm-II, and Strength Pareto evolutionary algorithm-II, while optimizing a benchmark combined heat and power system with two conflicting objectives. The most effective algorithm is determined based on the statistical parameters evaluated from 30 runs of execution, considering the hypervolume indicator and the average computational time as the performance criteria. A comparative assessment shows that the Pareto envelope-based selection algorithm-II is superior to the other two algorithms. Further, in this study, a multi-criteria decision analysis is performed on the Pareto set obtained from the Pareto envelope-based selection algorithm-II, using the technique for order preference by similarity to an ideal solution, combined with the Entropy method. To show the advantages of multi-objective optimization, the optimal solutions are also compared with the base case, and previously published results of the benchmark problem corresponding to single-objective optimization. From the Pareto envelope-based selection algorithm-II derived optimal solutions, 15.82% increase in the exergy efficiency and 12.22% reduction in the system cost rate are achieved over the base case results. The present optimal exergy efficiency is 11.89% higher and the system cost rate is 1.9% lower than the single-objective based optimal results.

Development of a supply chain configuration model for new product development: a multi-objective solution approach

Early supplier involvement in new product development is strategically important for the success of most firms. We therefore discuss how to configure an appropriate supply chain network for new product development. First, we propose a supply chain configuration model under a multi-objective optimization framework. We then develop a solution procedure based on a multi-objective evolutionary algorithm, i.e. NSGA-II (Non-Sorting Genetic Algorithm-II), to minimize the total supply chain cost and time-to-market. The efficiency of the proposed solution procedure is discussed along with a laptop assembly case study. The results obtained from NSGA-II are also compared from SPEA-II (Strength Pareto Evolutionary Algorithm-II).

A multi-objective evolutionary scheme for control points deployment in intelligent transportation systems

One of the problems that hinder emergency in developing countries is the problem of monitoring a number of activities on inter-urban roadway networks. In the literature, the use of control points is proposed in the context of these countries in order to ensure efficient monitoring, by ensuring a good coverage while minimizing the installation costs as well as the number of accidents across these road networks. In this work, we propose an optimal deployment of these control points from several optimization methods based on some evolutionary multi-objective algorithms: the non-dominated sorting genetic algorithm-II (NSGA-II); the multi-objective particle swarm optimization (MOPSO); the strength Pareto evolutionary algorithm -II (SPEA-II); and the Pareto envelope based selection algorithm-II (PESA-II). We performed the tests and compared these deployments using Pareto front and performance indicators like the spread and hypervolume and the inverted generational distance (IGD). The results obtained show that the NSGA-II method is the most adequate in the deployment of these control points.

Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images.

COVID-19 has affected the whole world drastically. A huge number of people have lost their lives due to this pandemic. Early detection of COVID-19 infection is helpful for treatment and quarantine. Therefore, many researchers have designed a deep learning model for the early diagnosis of COVID-19-infected patients. However, deep learning models suffer from overfitting and hyperparameter-tuning issues. To overcome these issues, in this paper, a metaheuristic-based deep COVID-19 screening model is proposed for X-ray images. The modified AlexNet architecture is used for feature extraction and classification of the input images. Strength Pareto evolutionary algorithm-II (SPEA-II) is used to tune the hyperparameters of modified AlexNet. The proposed model is tested on a four-class (i.e., COVID-19, tuberculosis, pneumonia, or healthy) dataset. Finally, the comparisons are drawn among the existing and the proposed models.

Using modified metaheuristic algorithms to solve a hazardous waste collection problem considering workload balancing and service time windows

Hazardous wastes’ volume produced by human activities has increased in recent years. Consequently, associated risks involved in the treatment, recycling, disposing, and transportation of these hazardous materials have become more attractive for the researchers. In this study, we propose a new model for hazardous waste location routing problem. Appending the service time window and workload balance to the previous mathematical models can be taken into account as the major contributions of this study. Three objective functions including two systematic goals (cost and risk) and one social goal (workload balancing) have been considered for the model. Compatibility between wastes and a heterogeneous fleet of vehicles, which are rarely investigated in the literature, is discussed in this paper. Since the proposed model is classified as a multi-objective model, three multi-objective evolutionary algorithms, namely Non-dominated Sorting Genetic Algorithm II (NSGA-II), Pareto Envelope-based Selection Algorithm II (PESA-II), and Strength Pareto Evolutionary Algorithm II (SPEA-II) are employed. As two other innovations, an adaptive penalty function is developed and the PESA-II is modified by removing replicated solutions from its archive and their obtained results are discussed. Finally, by experimenting a number of test problems in different sizes, it is demonstrated that proposed modified PESA-II and SPEA-II perform better than NSGA-II in most of comparison metrics including feasible answers exploration, CPU time, spacing metric, inverted generational distance, quality metric, etc., whereas, NSGA-II creates more spread Pareto frontiers which are suitable for decision-maker to choose, from among a range of different options.

Hybrid method for achieving Pareto front on economic emission dispatch

In this paper hybrid method, Modified Nondominated Sorted Genetic Algorithm (MNSGA-II) and Modified Population Variant Differential Evolution(MPVDE) have been placed in effect in achieving the best optimal solution of Multiobjective economic emission load dispatch optimization problem. In this technique latter, one is used to enforce the assigned percent of the population and the remaining with the former one. To overcome the premature convergence in an optimization problem diversity preserving operator is employed, from the tradeoff curve the best optimal solution is predicted using fuzzy set theory. This methodology validated on IEEE 30 bus test system with six generators, IEEE 118 bus test system with fourteen generators and with a forty generators test system. The solutions are dissimilitude with the existing metaheuristic methods like Strength Pareto Evolutionary Algorithm-II, Multiobjective differential evolution, Multi-objective Particle Swarm optimization, Fuzzy clustering particle swarm optimization, Nondominated sorting genetic algorithm-II.

A novel multi-objective spiral optimization algorithm for an innovative solar/biomass-based multi-generation energy system: 3E analyses, and optimization algorithms comparison

A novel multi-generation energy system is proposed consisting of a solar gas turbine system, multi-effect seawater desalination, LNG cold energy recovery unit, and a double effect absorption chiller. In addition, different working fluids of the ORC system are examined to select the suitable working fluid in terms of global warming potential and exergy efficiency of the system. Subsequently, energy, exergy, and economic (3E) analyses are performed to comprehensively evaluate the energy system. Besides, a parametric study is conducted to assess the effect of the most influential decision variables on the proposed system. Afterward, the novel multi-objective spiral optimization (MOSPO) algorithm is introduced to minimize total cost rate of the system while maximizing the exergy efficiency as the conflicting objective functions. The proposed algorithm is developed to optimize the decision variables effectively. To ascertain the final optimum solution point, three conventional methods i.e. TOPSIS, LINMAP and Shannon’s entropy are implemented. The results revealed that exergy efficiency and total cost rate of the system at the baseline are 60.05%, and 36.75 $/h, respectively. Furthermore, the net power output of the system would be 106.5 kW in addition to 0.7703 kW heating load, 56.01 kW cooling capacity, and 35.74 kg/h fresh water production capacity. The eco-environmental assessment revealed the fact that the proposed renewable-based energy system is capable of avoiding 485 tons CO2 emissions annually, and product cost rate reduction up to 6 $/hr in comparison to coal and natural gas-based energy systems. Besides, the proposed MOSPO algorithm is compared with common optimization methods; accordingly, the conventional algorithms are selected for the comparison including non-dominated sorting genetic algorithm II (NSGA-II), the multiple objective particle swarm optimization (MOPSO) algorithm, the Pareto envelope-based selection algorithm II (PESA-II), and the strength Pareto evolutionary algorithm II (SPEA-II). The comparison results show that the proposed MOSPO algorithm is preferable according to the Taylor Diagrams showing the performance of the algorithms.