Multi-objective Particle Swarm Optimization Approach Research Articles

An improved indirect adaptive neural control performance based on MOPSO approach: An experimental validation via a transesterification reactor

This paper proposes an indirect adaptive control method based on recurrent neural networks. To achieve satisfactory closed-loop performances, a neural emulator (NE) and a neural controller (NC) adapting rates are established using the multiobjective particle swarm optimization (MOPSO) algorithm. The proposed MOPSO algorithm has been designed to minimize, simultaneously, two separated objective functions: the emulation and the tracking errors. The proposed approach guarantees that the NE tracks the system dynamics within a short time window. Consequently, it provides for the suggested control structure useful information to synthesize optimal adaptive rates of the NE and NC. To validate the effectiveness of the proposed MOPSO algorithm, a numerical example and an experimental validation on a chemical reactor are proposed.

Multi-criteria assessment and optimization of a biomass-based cascade heat integration toward a novel multigeneration process: Application of a MOPSO method

In this study, a novel biomass-driven cascade heat integration scheme is devised, encompassing combined cooling, heating, and power generation, with a unique focus on the production of a secondary product (freshwater). The process commences with the establishment of a biomass digester, yielding biogas fuel to kickstart the integrated system. Subsequently, the utilization of a gas turbine cycle is introduced, and its waste heat is efficiently harnessed through a supercritical carbon dioxide process, a double-effect refrigeration cycle, and a heating terminal. Additionally, a modified organic Rankine cycle is incorporated to recover heat from the supercritical carbon dioxide process, while reverse osmosis desalination forms an integral part of the equipment lineup. The entire system is rigorously evaluated from both thermodynamic and economic standpoints, undergoing optimization across five distinct scenarios. A multi-objective particle swarm optimization approach, featuring two decision-making algorithms, is deployed in this optimization process. The primary objective is exergetic efficiency, while the secondary objectives encompass net output power, cooling, heating, sum unit cost of products, and net present value, each considered individually. The results of the study reveal that the highest attainable optimal exergy efficiency stands at 38.54 % when the secondary objective is net output power. Furthermore, the optimal values for the secondary objectives are determined to be 41112.59 kW, 1383.75 kW, 28413.97 kW, 18.72 $/GJ, and 75.17 M$, respectively. These findings underscore the potential of the proposed biomass-driven cascade heat integration system in achieving efficient and sustainable energy generation while simultaneously producing valuable secondary products.

Designing a Log-Logistic-Based EWMA Control Chart Using MOPSO and VIKOR Approaches for Monitoring Cardiac Surgery Performance

This study aims to develop a risk-adjusted Exponentially Weighted Moving Average (EWMA) control chart for computer-based performance monitoring in cardiac surgery. Patients have distinct risk factors that impact the surgical process before it even begins. As a result, risk adjustment is carried out utilizing the Accelerated Failure Time (AFT) model to consider these factors. Before using the risk-adjusted EWMA chart, the optimal parameter design should be established, considering the required statistical and economic factors. A model for Multi-Objective Decision Making (MODM) with multiple assignable causes has been proposed to accomplish this. The model is solved using a two-stage methodology based on the Multi-Objective Particle Swarm Optimization (MOPSO) method and the VIekriterijumsko KOmpromisno Rangiranje (VIKOR) method. An actual case study for cardiovascular patients has been undertaken to demonstrate the performance and effectiveness of the suggested model. The multi-objective and pure economic models have been thoroughly compared. The economic model with statistical constraints and the multi-objective model have also been compared again. The findings suggest that the multi-objective design of the risk-adjusted EWMA chart exhibits higher statistical performance in both cases against a small augment in cost.

Optimized lower pressure limit for condensate underground gas storage using a dynamic pseudo-component model

In the context of gas injection in reservoirs, the dilution process of components' thermodynamic properties has not been adequately represented in traditional numerical simulation methods. This study focuses on the Dalaoba condensate underground gas storage (CUGS) in China and establishes a dynamic pseudo-component model to identify the equilibrium point for gas storage facility efficiency and the condensate oil recovery rate. Using a multi-objective particle swarm optimization approach, thermodynamic parameters are estimated as functions of the injected gas volume. This dynamic pseudo-component model is then utilized to find the optimized lower pressure limit for CUGS by assessing condensate oil production, gas flow capability, and water encroachment. Results show that the dynamic pseudo-component model can represent the process of thermodynamic property changes towards lighter components during natural gas injection. The case study demonstrates that conventional models overestimate condensate oil production, while the dynamic model achieves an accuracy rate of 92.8 % with historical data. The dynamic pseudo-component model offers valuable insights for large-scale gas injection simulation, promoting stable and efficient natural gas storage and supply in CUGS.

Optimal Sizing of Renewable Energy Communities: A Multiple Swarms Multi-Objective Particle Swarm Optimization Approach

Renewable energy communities have gained popularity as a means of reducing carbon emissions and enhancing energy independence. However, determining the optimal sizing for each production and storage unit within these communities poses challenges due to conflicting objectives, such as minimizing costs while maximizing energy production. To address this issue, this paper employs a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm with multiple swarms. This approach aims to foster a broader diversity of solutions while concurrently ensuring a good plurality of nondominant solutions that define a Pareto frontier. To evaluate the effectiveness and reliability of this approach, four case studies with different energy management strategies focused on real-world operations were evaluated, aiming to replicate the practical challenges encountered in actual renewable energy communities. The results demonstrate the effectiveness of the proposed approach in determining the optimal size of production and storage units within renewable energy communities, while simultaneously addressing multiple conflicting objectives, including economic viability and flexibility, specifically Levelized Cost of Energy (LCOE), Self-Consumption Ratio (SCR) and Self-Sufficiency Ratio (SSR). The findings also provide valuable insights that clarify which energy management strategies are most suitable for this type of community.

Strategic Sizing and Placement of Distributed Generation in Radial Distributed Networks Using Multiobjective PSO

Distributed generators (DGs) offer significant advantages to electric power systems, including improved system losses, stability, and reduced losses. However, realizing these benefits necessitates optimal DG site selection and sizing. This study proposes a traditional multiobjective particle swarm optimization (PSO) approach to determine the optimal location and size of renewable energy-based DGs (wind and solar) on the Namibian distribution system. The aim is to enhance voltage profiles and minimize power losses and total DG cost. Probabilistic models are employed to account for the random nature of wind speeds and solar irradiances. This is used in an algorithm which eventually optimizes the siting and sizing of DGs using the nearest main substation as reference. The proposed method is tested on the Vhungu-Vhungu 11 kV distribution network in Namibia. Four cases were considered: base case with no DG, solar power, wind power, and a hybrid of both wind and solar. Optimal values for each case are determined and analyzed: 0.69.93 kW at 26 km for solar PV-based DG and 100 kW at 42 km for wind-based DG. These findings will serve as a valuable blueprint for future DG connections on the Namibian distribution network, providing guidance for optimizing system performance.

Evaluation and optimization of a horizontal well-triplet for CO2 plume geothermal harvest: Comparison between open and close reservoirs

Depleted oil/gas reservoirs can be used for CO2 sequestration and utilization; CO2 plume geothermal (CPG) production from a CO2 geological storage reservoir has emerged as a viable approach owing to its favorable thermodynamic properties. However, most oil/gas reservoirs are separated by faults into geological blocks, and the impact of these fault boundaries on CPG performance remains unclear. This study evaluated and compared the technical performances of a horizontal well-triplet placement and CPG operation in a thin geothermal reservoir between open and close boundaries. A meta-model simulation–optimization framework was also applied for the economic assessment of both systems. For each boundary system, 200 samples of the horizontal well-triplet design, including injection well overpressure, horizontal perforation length of injection and production wells, and distance between injection and production well, were derived from a 4D-parameter space constrained by particular ranges. These 200 samples of input parameters yielded 200 numerical models for the simulation of CPG operations. A suite of technical metrics, including lifespan, injected, produced, and stored CO2, recovered heat energy, and produced heat flux, were computed from the model simulations for technical performance evaluations. Meta models for economic objectives were developed based on the 200 input–output datasets and used in optimization. A multi-objective particle swarm optimization (MOPSO) approach was utilized to obtain the appropriate optimal designs of the well-triplet for the two boundary systems. The results indicated that performance of both systems was controlled by the well space, which was positively correlated to CO2 storage and total heat recovery but negatively to power plant capacity. High horizontal well lengths and overpressure facilitated CO2 storage, heat recovery, and plant capacity. The open system could generate much more overall profit than the close system owing to CO2 storage income.

Integrated Intelligent Method Based on Fuzzy Logic for Optimizing Laser Microfabrication Processing of GnPs-Improved Alumina Nanocomposites

Studies on using multifunctional graphene nanostructures to enhance the microfabrication processing of monolithic alumina are still rare and too limited to meet the requirements of green manufacturing criteria. Therefore, this study aims to increase the ablation depth and material removal rate and minimize the roughness of the fabricated microchannel of alumina-based nanocomposites. To achieve this, high-density alumina nanocomposites with different graphene nanoplatelet (GnP) contents (0.5 wt.%, 1 wt.%, 1.5 wt.%, and 2.5 wt.%) were fabricated. Afterward, statistical analysis based on the full factorial design was performed to study the influence of the graphene reinforcement ratio, scanning speed, and frequency on material removal rate (MRR), surface roughness, and ablation depth during low-power laser micromachining. After that, an integrated intelligent multi-objective optimization approach based on the adaptive neuro-fuzzy inference system (ANIFS) and multi-objective particle swarm optimization approach was developed to monitor and find the optimal GnP ratio and microlaser parameters. The results reveal that the GnP reinforcement ratio significantly affects the laser micromachining performance of Al2O3 nanocomposites. This study also revealed that the developed ANFIS models could obtain an accurate estimation model for monitoring the surface roughness, MRR, and ablation depth with fewer errors than 52.07%, 100.15%, and 76% for surface roughness, MRR, and ablation depth, respectively, in comparison with the mathematical models. The integrated intelligent optimization approach indicated that a GnP reinforcement ratio of 2.16, scanning speed of 342 mm/s, and frequency of 20 kHz led to the fabrication of microchannels with high quality and accuracy of Al2O3 nanocomposites. In contrast, the unreinforced alumina could not be machined using the same optimized parameters with low-power laser technology. Henceforth, an integrated intelligence method is a powerful tool for monitoring and optimizing the micromachining processes of ceramic nanocomposites, as demonstrated by the obtained results.

Genetically-modified Multi-objective Particle Swarm Optimization approach for high-performance computing workflow scheduling

Nowadays, scientific research, industry, and many other fields are greedy regarding computing resources. Therefore, Cloud Computing infrastructures are now attracting pervasive interest thanks to their excellent hallmarks such as scalability, high performance, reliability, and the pay-per-use strategy. The execution of these high-performant applications on such kind of computing environments in respect of optimizing many conflicting objectives brings us to a challenging issue commonly known as the multi-objective workflows scheduling on large scale distributed systems. Having this in mind, we outline in the present paper our proposed approach called Genetically-modified Multi-objective Particle Swarm Optimization (GMPSO) for scheduling application workflows on hybrid Clouds in the context of high-performance computing in an attempt to optimize Makespan and Cost. The GMPSO consists of incorporating genetic operations into the Multi-objective Particle Swarm Optimization to enhance the resulting solutions. To achieve this, we have designed a novel solution encoding that represents the task ordering, the task mapping and the resource provisioning processes of the workflow scheduling problem in hybrid Clouds. In addition, a set of particular adaptive evolutionary operators have been designed. Conducted simulations lead to significant results compared with a set of well-performed algorithms such NSGA-II, OMOPSO and SMPSO, especially, for the most-demanding workload of workflows.

Model‐based multi‐objective particle swarm production optimization for efficient injection/production planning to improve reservoir recovery

AbstractThis study employs an adjusted version of the multi‐objective particle swarm optimization (MOPSO) algorithm to plan an optimized reservoir's injection/production strategy. Three case studies, including two water‐flooding benchmark models and one gas‐condensate problem, are exercised as subjected problems to validate the MOPSO approach. The contradicting values of objectives, long‐term net present value (LNPV) versus short‐term net present value (SNPV), are obtained so that relying on a Pareto front improves decision‐making. In one water‐flooded case, inflow control valves of smart wells are considered to be adjusted within the optimization, while in the second case, the optimized well injection rates are control variables. The obtained results for water‐flooded reservoirs are shown to optimize the competitive objective functions more than the previous efforts in the literature. Moreover, 10 different permeability maps of the second case are implemented to obtain the optimum injection rates to perform optimization under uncertainty. The MOPSO robustly optimized the production/injection strategy in the presence of model uncertainty. In the gas‐condensate problem, the optimal gas injection rate in the SPE‐3 benchmark model is determined. The gas‐condensate case's outputs yield a decreased oil saturation result in the reservoir compared to non‐optimized production scenarios. Results illustrate that for all cases, MOPSO can provide optimal injection/production scenarios. Therefore, the proposed scheme gives the advantage of deciding between the set of results into a decision‐maker to optimize the production program by trading‐off within different strategies. Besides the Pareto fronts with adequate variety and steadiness, a great converging rate is the main advantage of this method.

FUPE: A security driven task scheduling approach for SDN-based IoT–Fog networks

Fog computing is a paradigm to overcome the cloud computing limitations which provides low latency to the users’ applications for the Internet of Things (IoT). Software-defined networking (SDN) is a practical networking infrastructure that provides a great capability in managing network flows. SDN switches are powerful devices, which can be used as fog devices/fog gateways simultaneously. Hence, fog devices are more vulnerable to several attacks. TCP SYN flood attack is one of the most common denial of service attacks, in which a malicious node produces many half-open TCP connections on the targeted computational nodes so as to break them down. Motivated by this, in this paper, we apply SDN concepts to address TCP SYN flood attacks in IoT–fog networks . We propose FUPE, a security-aware task scheduler in IoT–fog networks. FUPE puts forward a fuzzy-based multi-objective particle swarm Optimization approach to aggregate optimal computing resources and providing a proper level of security protection into one synthetic objective to find a single proper answer. We perform extensive simulations on IoT-based scenario to show that the FUPE algorithm significantly outperforms state-of-the-art algorithms. The simulation results indicate that, by varying the attack rates, the number of fog devices, and the number of jobs, the average response time of FUPE improved by 11% and 17%, and the network utilization of FUPE improved by 10% and 22% in comparison with Genetic and Particle Swarm Optimization algorithms, respectively.

Improved InxGa1_xP/GaAs /Ge tandem solar cell using light trapping engineering and multi-objective optimization approach

In this paper, an analytical model for studying the effect of light trapping mechanism on tandem solar cell performance is developed. The proposed model considers diffraction grating morphology and antireflection coating of the InxGa1_xP/GaAs/Ge tandem solar cell. The main photovoltaic figures of merit of the InxGa1_xP/GaAs/Ge tandem solar cell are investigated. The obtained results prove the outstanding capability of the light trapping mechanism to improve device performance. An efficiency of 32.5% was obtained. A short circuit current density (JSC) of 28 mA/cm2, an open-circuit voltage (VOC) of 1.288 V, and a fill factor (FF) of 87.7% were calculated. In addition, the developed model serves as a fitness function to optimize the light trapping capability using a multi-objective particle swarm optimization (MOPSO) approach. The optimized tandem solar cell design exhibits higher performance characterized by JSC =35.3 mA/cm2, VOC = 1.305 V, and a conversion efficiency of 41.7% which outweighs that of the conventional planar solar cell. Therefore, the proposed design methodology efficiently minimize the reflectance via establishing an intensive light trapping mechanism at the front of both subcells and opens promising opportunities to enhance the tandem solar cell performances.

Optimal design parameters and performance optimization of thermodynamically balanced dish/Stirling concentrated solar power system using multi-objective particle swarm optimization

In this work, a theoretical model based on opto-geometric and thermodynamic analyses of Solar Dish-Stirling System (SDSS) considering opto-geometric sizing criteria and energy balance of different system components has been developed. An optimization algorithm has been also implemented for simultaneously maximizing the output power and total efficiency of the SDSS using the Multi-Objective Particle Swarm Optimization (MOPSO). Nine decision variables have been considered, namely, the interception factor, mirror reflectance of the concentrator, receiver absorbance, receiver transmittance, receiver emissivity, tilt angle of the receiver, the direct solar intensity, the speed of wind, and the temperature of ambient. Pareto optimal frontier (POF) was determined and the optimal solution was selected by applying a decision making approach called the Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP). Moreover, a sensitivity analysis was also carried out to identify the impact of the rim angle, the dish concentrator diameter, the mirror soiling coefficient of concentrator, and the ratio of design concentration on the SDSS performance optimization. The results showed that the proposed MOPSO shows a feasible approach to achieve a maximal power output of 23.46 kW with an optimal final total efficiency of 30.15% that approaches the ideal solution. Moreover, the sensitivity results indicated that SDSS with concentrator diameters of (2.5–15 m) can produce final optimal output powers of (1.43–53.34 kW) with insignificant variation in the overall efficiency (29.80–30.20%), at the obtained optimal solutions for the optimized dish concentrator configurations. Moreover, the results also emphasize how mirror soiling factor of the concentrator, has a dramatic effect on the optimal electric power and total efficiency of SDSS. It can be concluded that the proposed MOPSO approach can lead to more desired results and allow the technical feasibility for design of SDSS according to the need of such output power required for a certain application.

Multi-Objective Pareto Histogram Equalization

Several histogram equalization methods focus on enhancing the contrast as one of their main objectives, but usually without considering the details of the input image. Other methods seek to keep the brightness while improving the contrast, causing distortion. Among the multi-objective algorithms, the classical optimization (a priori) techniques are commonly used given their simplicity. One of the most representative method is the weighted sum of metrics used to enhance the contrast of an image. These type of techniques, beside just returning a single image, have problems related to the weight assignment for each selected metric. To avoid the pitfalls of the algorithms just mentioned, we propose a new method called MOPHE (Multi-Objective Pareto Histogram Equalization) which is based on Multi-objective Particle Swarm Optimization (MOPSO) approach combining different metrics in a posteriori selection criteria context. The goal of this method is three-fold: (1) improve the contrast (2) without losing important details, (3) avoiding an excessive distortion. MOPHE, is a pure multi-objective optimization algorithm, consequently a set of trade-off optimal solutions are generated, thus providing alternative solutions to the decision-maker, allowing the selection of one or more resulting images, depending on the application needs. Experimental results indicate that MOPHE is a promising approach, as it calculates a set of trade-off optimal solutions that are better than the results obtained from representative algorithms from the state-of-the-art regarding visual quality and metrics measurement.

Parametric performance optimization of three sides roughened solar air heater

ABSTRACTSolar clean energy can be harnessed by several methods using technologies like solar heating, photovoltaic cell, solar architecture, photosynthesis. Solar energy is converted either by active technologies or by passive technologies by the method they capture and distribute solar energy. In view of this, the paper represents the optimization result on Nusselt number and thermal efficiency for 3 sides roughened solar air heater using the hybrid approach of response surface method (RSM) and multi objective particle swarm optimization (MOPSO) algorithm. The values of input parameters are taken according to the experimental values. The value of the output parameters, Nusselt Number and thermal efficiency are found to be in the range of 65–80 and 63–75%, respectively, for the optimum value of the input parameters are Reynolds number, 12000–13000, the relative roughness pitch, 10, the relative roughness height 0.03–0.04 and the rate of mass flow is 0.04 kg/s using MOPSO approach, and the optimum solution is identified by application of maximum deviation theory (MDT) approach. A confirmative test has been conducted to validate the optimum results obtained with an error of 3.39 percentages.

Particle Swarm Optimization for Cooperative Multi-Robot Task Allocation: A Multi-Objective Approach

This letter presents a new Multi-Objective Particle Swarm Optimization (MOPSO) approach to a Cooperative Multi-Robot Task Allocation (CMRTA) problem, where the robots have to minimize the total team cost and, additionally, balance their workloads. We formulate the CMRTA problem as a more complex variant of multiple Travelling Salesman Problems (mTSP) and, in particular, address how to minimize the total travel distance of the entire robot team, as well as how to minimize the highest travel distance of an individual robot. The proposed approach extends the standard single-objective Particle Swarm Optimization (PSO) to cope with the multiple objectives, and its novel feature lies in a Pareto front refinement strategy and a probability-based leader selection strategy. To validate the proposed approach, we first use three benchmark functions to evaluate the performance of finding the true Pareto fronts in comparison with four existing well-known algorithms in continuous spaces. Afterwards, we use six datasets to investigate the task allocation mechanisms in dealing with the CMRTA problem in discrete spaces.

Multi-objective clustering algorithm using particle swarm optimization with crowding distance (MCPSO-CD)

Clustering, an unsupervised method of grouping sets of data, is used as a solution technique in various fields to divide and restructure data to become more significant and transform them into more useful information. Generally, clustering is difficult and complex phenomenon, where the appropriate numbers of clusters are always unknown, comes with a large number of potential solutions, and as well the datasets are unsupervised. These problems can be addressed by the Multi-Objective Particle Swarm Optimization (MOPSO) approach, which is commonly used in addressing optimization problems. However, MOPSO algorithm produces a group of non-dominated solutions which make the selection of an “appropriate” Pareto optimal or non-dominated solution more difficult. According to the literature, crowding distance is one of the most efficient algorithms that was developed based on density measures to treat the problem of selection mechanism for archive updates. In an attempt to address this problem, the clustering-based method that utilizes crowding distance (CD) technique to balance the optimality of the objectives in Pareto optimal solution search is proposed. The approach is based on the dominance concept and crowding distances mechanism to guarantee survival of the best solution. Furthermore, we used the Pareto dominance concept after calculating the value of crowding degree for each solution. The proposed method was evaluated against five clustering approaches that have succeeded in optimization that comprises of K-means Clustering, MCPSO, IMCPSO, Spectral clustering, Birch, and average-link algorithms. The results of the evaluation show that the proposed approach exemplified the state-of-the-art method with significant differences in most of the datasets tested.

Optimal energy management of microgrid using advanced multi-objective particle swarm optimization

Purpose The purpose of this paper is to propose an optimal energy management (OEM) method using intelligent optimization techniques applied to implement an optimally hybrid heat and power isolated microgrid. The microgrid investigated combines renewable and conventional power generation. Design/methodology/approach Five bio-inspired optimization methods include an advanced proposed multi-objective particle swarm optimization (MOPSO) approach which is comparatively applied for OEM of the implemented microgrid with other bio-inspired optimization approaches via their comparative simulation results. Findings Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization methods. Moreover, the proposed MOPSO is successfully applied to perform 24-h OEM microgrid. The simulation results also display the merits of the real time optimization along with the arbitrary of users’ selection as to satisfy their power requirement. Originality/value This paper focuses on the OEM of a designed microgrid using a newly proposed modified MOPSO algorithm. Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization approaches.

Multi-Objective Particle Swarm for Optimal Load Shedding Remedy Strategies of Power System

Load shedding is an emergency strategy that mitigates substantial mismatch between the generation and the loads. It generally sustains system stability during/after severe disturbances. This article proposes robust, simple, and innovative Under-Frequency Load Shedding (UFLS) technique based on Multi-Objective Particle Swarm Optimization (MOPSO). MOPSO has the objectives of: minimizing the amount of the dropped load and maximizing the lowest swing frequency. The functionality and feasibility of the proposed MOPSO are corroborated via comprehensive comparison with traditional, adaptive, Single Objective PSO (SOPSO), and Genetic Algorithm (GA) UFLS schemes. IEEE 9-bus and 39-bus systems are used cases for examining the reliability, applicability, and viability of the proposed MOPSO. Different scenarios as: outage of single, multiple generating plants and load increase are applied in the test systems, while load shedding is executed via MOPSO, SOPOS, GA, traditional, and adaptive UFLS approaches. The DigSilent power factor software is used for simulating the test systems while subjected to the different disturbance levels. MATLAB is used for coding SOPSO, MOPSO, adaptive, and GA algorithms. The results show that MOPSO-based UFLS relay produces higher lowest swing frequency and lower amount of dropped load than SOPSO and GA. MOPSO requires less computation requirements than GA approach.

A multi-objective optimization approach for brain MRI segmentation using fuzzy entropy clustering and region-based active contour methods

In this paper, we present a new multi-objective optimization approach for segmentation of Magnetic Resonance Imaging (MRI) of the human brain. The proposed algorithm not only takes advantages but also solves major drawbacks of two well-known complementary techniques, called fuzzy entropy clustering method and region-based active contour method, using multi-objective particle swarm optimization (MOPSO) approach. In order to obtain accurate segmentation results, firstly, two fitness functions with independent characteristics, compactness and separation, are derived from kernelized fuzzy entropy clustering with local spatial information and bias correction (KFECSB) and a novel adaptive energy weight combined with global and local fitting energy active contour (AWGLAC) model. Then, they are simultaneously optimized to finally produce a set of non-dominated solutions, from which L2-metric method is used to select the best trade-off solution. Our algorithm is both verified and compared with other state-of-the-art methods using simulated MR images and real MR images from the McConnell Brain Imaging Center (BrainWeb) and the Internet Brain Segmentation Repository (IBSR), respectively. The experimental results demonstrate that the proposed technique achieves superior segmentation performance in terms of accuracy and robustness.