Contradictory Objective Functions Research Articles

Multi-objective design optimization of an in-pipe inspection robot

In addition to inspection, In-Pipe Inspection Robots (IPIRs) could be enabled to perform other tasks, such as object retrieval and blockage clearing. In this study, a new IPIR is proposed with a design aimed at expanding its range of potential applications beyond inspection. The design methodology followed to facilitate the aforementioned application expansion commences with the development of a parametric preliminary design of an IPIR, in which the sizing of the robot parts are determined based on an optimization problem formulated in this work. The multi-objective optimization problem maximizes two contradictory objective functions, namely, its payload capacity, and the range of pipe diameters in which it can fit (radial range). The problem's Pareto front is solved using the NSGA-II implemented by Matlab's gamultiobj(.). The robot's final design is selected based on one of the Pareto-optimal solutions obtained from the Pareto front with a theoretical payload capacity of 11 kg and a radial range of 33 mm. The robot was manufactured and tested to validate such theoretical results.

A multi-objective optimization algorithm for flow shop group scheduling problem with sequence dependent setup time and worker learning

The optimization of production systems has become increasingly important in manufacturing industries due to the growing competition and market demands. One of the overriding concerns of managers is the efficient exploitation of workers' learning to increase output and decrease downtime. The learning effect symbolizes the improvement of workers' ability and performance through the repetition of similar jobs. On the other hand, it is a critical requirement for decision-makers to have effective management of the transportation phase to achieve an optimal production plan. This paper considers a flow shop sequence-dependent group scheduling problem (FSDGS) with a learning effectto minimize two contradictory objective functions, namely makespan and energy consumption.A mixed-integer linear programming model is proposed to find optimal jobs, group schedules, and appropriate production and transportation speeds to enhance the overall performance of the system. Due to the complexity of the planning process, we propose lower bounds and an efficient resolution method based on multi-objective simulated annealing metaheuristic (MOSA), enhanced by a local search procedure to tackle this problem. The proposed method is evaluated through several experiments based on a real case study, using different learning rates and setup time ratio levels. The obtained results demonstrate the effectiveness of the algorithm in improving the performance of production systems by reducing processing time and energy consumption. These findings have significant implications for the design and optimization of production systems in manufacturing industries.

An innovative compressed air energy storage (CAES) using hydrogen energy integrated with geothermal and solar energy technologies: A comprehensive techno-economic analysis - different climate areas- using artificial intelligent (AI)

The present study evaluates the optimal design of a renewable system based on solar and geothermal energy for power generation and cooling based on a solar cycle with thermal energy storage and an electrolyzer to produce hydrogen fuel for the combustion chamber. The subsystems include solar collectors, gas turbines, an electrolyzer, an absorption chiller, and compressed air energy storage. The solar collector surface area, geothermal source temperature, steam turbine input pressure, and evaporator input temperature were found to be major determinants. The economic analysis of the system showed that the solar subsystem, steam Rankine cycle, and compressed air energy storage accounted for the largest portions of the cost rate. The exergy analysis of the system demonstrated that the solar subsystem and SRC had the highest contributions to total exergy destruction. A comparative case study was conducted on Isfahan, Bandar Abbas, Mashhad, Semnan, and Zanjan in Iran to evaluate the performance of the proposed system at different ambient temperatures and irradiance levels during the year. To optimize the system and find the optimal objective functions, the NSGA-II algorithm was employed. The contradictory objective functions of the system included exergy efficiency maximization and cost rate minimization. The optimal Exergy round trip efficiency and cost rate were found to be 29.25% and 714.25 ($/h), respectively.

Game Algorithm of Intelligent Driving Vehicle Based on Left-Turn Scene of Crossroad Traffic Flow.

Vehicle networking and autonomous driving are hot areas of scientific research today, and they complement each other and play an important role in people's intelligent travel. Intelligent driving vehicle can enhance road safety, effectively reduce traffic flow and fuel consumption, and promote the overall social development. It has great application value in urban traffic system. The traffic condition of a city directly affects the economic development of the city and the improvement of people's quality of life. As the “core” of the urban traffic network, intersections are the frequent places where traffic jams occur. Game theory, as a win-win theory, mainly solves the problem of multiperson and multi-objective with contradictory objective functions and can be used to study the optimal signal control strategy. Aiming at this problem, the potential conflict behaviors of intelligent driving vehicles when turning left at urban intersections are analyzed and a decision model is established. A long-term trajectory prediction model of straight vehicles is established based on the Gaussian process regression model (GPR) considering the vehicle motion pattern. Combined with trajectory prediction, a decision-making process (model) for intelligent driving vehicles based on conflict resolution and a multifactor driving action selection method are proposed. A coordination algorithm based on game theory is designed for conflicting vehicles. The proposed algorithm is verified by the self-developed intelligent vehicle hardware simulation platform. The simulation results show that the PID method based on digital identification and positioning makes the intelligent vehicle obtain good system step response, can improve the disturbance tracking ability of intersection turning analysis, meet the requirements of turning control system, and reduce the complexity and randomness of parameter design, which is better than the traditional fuzzy control method.

A multi-objective natural aggregation algorithm for optimizing user allocation matrix in visible light communication

Allocating users to LED panels in a visible light communication (VLC) environment is a non-convex multi-objective constrained optimization problem. The computational complexity and accuracy of the optimization are the key factors to deal with in a VLC environment. In this manuscript, initially the user allocation problem is formulated as a single objective optimization problem by using sum-utility function of data rate and blockage probability. Then these two contradictory objective functions are simultaneously optimized by a new multi-objective natural aggregation algorithm (MONAA). This proposed algorithm is inspired by the aggregation behavior of cockroaches which help them to determine the darkest spot in a search space. The performance of this multi-objective algorithm is compared with NSGA-II, MOPSO, MOEA/D and MOSOS on eight unconstrained and six constrained mathematical function optimizations. In single objective and multi-objective cases, superior performance is demonstrated by the natural aggregation algorithm for solving the user allocation matrix optimization.

Development Of Multi-Criteria Decision Making Model In Packed Beverage Industry Using Global Criterion Method

The packaged beverage industry with various brands has appeared on the market. The number of competitors in the market requires the packaged beverage industry to implement the right strategy to win the competition. One strategy that can be applied is to make savings and maximize the use of company resources. One of the savings that can be done is to minimize production costs, while maximizing the use of resources can be done by maximizing the utility of the machine owned by the company. Efforts to minimize production costs can be contradictory to efforts to maximize machine utility, this is because maximizing machine utility will certainly require additional production costs, even though the company also wants to minimize production costs. This is included in the category of multi-criteria decision-making problems. To facilitate the company's management in achieving these contradictory company goals, this study will develop a multi-criteria decision-making model to assist companies in making optimal decisions regarding these contradictory company goals. The multi-criteria decision-making model developed is a mathematical model consisting of two mutually contradictory objective functions, namely minimizing production costs and maximizing machine utility, as well as problem constraints which are limitations that are owned by the company in optimizing the achievement of these contradictory company goals. To optimize the multi-criteria decision-making model that has been developed, it will use the global criterion method. The global criterion method will produce a compromise solution that has a minimum deviation from the ideal value of each objective function. Using the global criterion method, a production plan that minimizes production costs and maximizes machine utility is obtained. The multi-criteria decision-making model developed will cause the production plan to have a small deviation from the actual demand, because the model development involves forecasting product demand. The developed model will also produce a production plan that will not exceed the warehouse capacity, so that product storage costs can be minimized, this is because the developed model includes warehouse capacity as a barrier.

Fuzzy Clustering to Identify Clusters at Different Levels of Fuzziness: An Evolutionary Multiobjective Optimization Approach

Fuzzy clustering methods identify naturally occurring clusters in a dataset, where the extent to which different clusters are overlapped can differ. Most methods have a parameter to fix the level of fuzziness. However, the appropriate level of fuzziness depends on the application at hand. This paper presents an entropy c -means (ECM), a method of fuzzy clustering that simultaneously optimizes two contradictory objective functions, resulting in the creation of fuzzy clusters with different levels of fuzziness. This allows ECM to identify clusters with different degrees of overlap. ECM optimizes the two objective functions using two multiobjective optimization methods, nondominated sorting genetic algorithm II (NSGA-II) and multiobjective evolutionary algorithm based on decomposition (MOEA/D). We also propose a method to select a suitable tradeoff clustering from the Pareto front. Experiments on challenging synthetic datasets as well as real-world datasets show that ECM leads to better cluster detection compared to the conventional fuzzy clustering methods as well as previously used multiobjective methods for fuzzy clustering.

Urban water supply system optimization and planning: Bi-objective optimization and system dynamics methods

Sustainable, low-cost, environmentally conscious development is a major challenge for urban water supply systems. This study considers household and industrial water demand and establishes a bi-objective model to balance the economic and environmental cost of a three-stage urban water supply system including water production, distribution, and treatment processes. Considering different attitudes toward the increasing imbalance between water demand and supply, three hypothetical scenarios are designed based on which three sets of data on water demand and wastewater production volumes are defined. Then, a two-step method is applied to obtain solutions to decision variables (potable water use rate and wastewater untreated rate). After optimization, a system dynamics model is applied to test water supply system performance under short-, medium-, and long-term planning scenarios. Finally, the model is applied to Singapore to test its feasibility. The proposed bi-objective model provides managerial insights into the use of alternative water resources. For example, in 2020, water demand is simulated as 5.63×108m3. After optimization using the proposed model, the quantity of alternative water is 2.5×108m3 (water from the local catchment), 2.5×108m3 (imported water), 5.31×107m3 (desalinated water), 4.05×107m3 (NEWater for indirect potable use), and 8.10×107m3 (NEWater for direct non-potable use). Total water supply is 6.75×108m3, which means there is some redundancy in water supply. Further, three indexes—supply-demand ratio, economic cost, and environmental cost—help to identify the suitable planning period: the medium-term focus is the most appropriate option as it promises water adequacy for over 15 years and has lower energy costs and less negative environmental impact during the evaluation years. In addition, this study proves the influence of decision makers’ preferences on contradictory objective functions (economic cost minimization and environmental negative impact minimization). Overall, the proposed model balances a trade-off between the risk of an increasing future supply-demand imbalance and current water adequacy. Lastly, the proposed model offers a paradigm to identify a sustainable planning period.

Multi-objective optimization for location-based and preferences-aware recommendation

Location is of vital importance in recommender systems. This paper proposes a novel multi-objective framework for location-based and preference-aware recommendation. Under this framework, location-based recommendation is modeled as a multiobjective optimization problem and two contradictory objective functions are taken into consideration. One objective function aims to depict the performance of recommendation algorithm to recommend similar items. Another objective function reflects the ability of recommendation algorithm to recommend diverse items. It is a challenge to optimize these two contradictory objective functions simultaneously. In this paper, a novel multi-objective evolutionary algorithm is proposed to solve this modeled multiobjective optimization problem. The proposed algorithm can return a series of high-quality recommendation candidate solutions in one run and every solution is a trade-off between these two objective functions. The proposed algorithm is applied on two real recommendation scenarios: movie recommendation and music recommendation. Experimental results show that the proposed algorithm can produce recommendation solutions which are much better than those produced by existing algorithms in location-based recommendation and the proposed algorithm also has great performance in alleviating data sparsity and cold-start problems.

An incentive-based policy on reduction of GHG emissions and loss minimization using adaptive group search multi-objective optimization algorithm

A transactive strategy to purposeful pricing distributed energy resources (DERs) in distribution networks is proposed in this paper. This strategy is presented as a novel heuristic optimization approach. The total network loss and released greenhouse gases (GHGs) emissions are considered as objective functions. In addition, the locational marginal prices (LMPs) and power factors of DERs are considered as decision variables. Each DER, which is more participated in the mitigation of afore-mentioned objectives, will contribute a larger excitement form benefits consequently. Therefore, more contribution consequent to more generation leads to a higher price for DERs bus in comparison to substation market price. Also, the earned benefits from loss/emission mitigations are allocated to DERs directly. The fairness of this pricing process is supervised by the Independent Distribution System Operator (IDSO). Because the problem has two contradictory objective functions, a reliable Multi-Objective method called Chaotic search and Covariance matrix (MGSOACC) is proposed to solve the problem. To evaluate the proposed method, the pricing procedure is applied on modified IEEE-33 and IEEE-69 bus test networks. Furthermore, in order to the validation of the proposed optimization method, the result-oriented comparisons between four conventional Multi-Objective optimization methods and proposed optimization method are presented.

A new coordinated design of sectionalizing scheme and load restoration process considering reliability of transmission system

One of the most important processes during load restoration is to use a suitable sectionalizing scheme to enhance the stability, especially when two islands are synchronized. In this regard, this paper provides a new scheme for the coordination of sectionalizing and load restoration to enhance the power system performance before and during the synchronization of islands. In doing so, there will be contradictory objective functions including maximizing the quality of sectionalizing process, minimizing the maximum standing phase angle (SPA) between islands, and minimizing the sum of energy not supplied (ENS) with unavailable energy capability (UEC) between all N-1 contingencies. Therefore, a multi-objective problem is defined as a mixed integer non-linear problem (MINLP). Also, a reliability-based index is defined to determine the quality of each island. Then, the θ-based water cycle algorithm (θ-WCA) is used to obtain the best Pareto optimal set. Two IEEE 39-bus and 118-bus power systems are used for validating the proposed method. The simulation results imply that the system can benefit from this scheme not only to have the good quality of sectionalizing, but also to enhance the power system performance during load restoration and the synchronization of islands.

Power System Transient Stability: An Algorithm for Assessment and Enhancement Based on Catastrophe Theory and FACTS Devices

It is of crucial importance to obviate power system damage and cascading failures that may cause a full or partial blackout when the system is exposed to severe contingencies. Flexible alternating current transmission system (FACTS) devices have been harnessed for solving several power system problems including transient stability. Ever since, to emphasize the effectiveness of the FACTS technology, the number and allocation of these devices must be selected properly. So, a novel algorithm is proposed in this paper to determine the best least number (BLN) and allocation of the thyristor-controlled series capacitor (TCSC) with a goal of improving the transient stability in an optimal manner. A combination of the catastrophe theory (CT) and the multi-objective particle swarm optimization (MOPSO) method in addition to a clustering technique is used to structure the proposed algorithm. The CT is used to assess the transient stability and calculate the critical clearing time (CCT). MOPSO is applied to compromise between maximizing the CCT and minimizing the cost of TCSCs as two contradictory objective functions. The clustering technique is designed to provide the BLN of TCSC devices. Accordingly, at least investment, the proposed algorithm satisfies an increase of the stability margin by increasing the value of CCT for each generator and improves the location of operating points in the CT’s stability region. Simulation of the proposed algorithm application to New England 39-bus power system is presented to verify the algorithm effectiveness. The results confirm the feasibility of this algorithm and are validated in comparison with those obtained through time-domain simulation.

Design of optimal CMOS ring oscillator using an intelligent optimization tool

This paper presents an intelligent sizing method to improve the performance and efficiency of a CMOS ring oscillator (RO). The proposed approach is based on the simultaneous utilization of powerful and new multi-objective optimization techniques along with a circuit simulator under a data link. The proposed optimizing tool creates a perfect trade-off between the contradictory objective functions in CMOS RO optimal design. This tool is applied for intelligent estimation of the circuit parameters (channel width of transistors), which have a decisive influence on RO specifications. Along the optimal RO design in an specified range of oscillation frequency, the Power Consumption, Phase Noise, Figure of Merit, Integration Index, Design Cycle Time are considered as objective functions. Also, in generation of Pareto front some important issues, i.e., Overall Nondominated Vector Generation, and Spacing are considered for more effectiveness of the obtained feasible solutions in application. Four optimization algorithms called Multi-Objective Genetic Algorithm, Multi-Objective Inclined Planes system Optimization, Multi-Objective Particle Swarm Optimization and Multi-Objective Modified Inclined Planes System Optimization (MOMIPO) are utilized for 0.18-mm CMOS technology with supply voltage of 1-V. Based on our extensive simulations and experimental results MOMIPO outperforms the best performance among other multi-objective algorithms in presented RO designing tool.

A Multi-Objective Approach to Fuzzy Clustering using ITLBO Algorithm

Data clustering is one of the most important areas of research in data mining and knowledge discovery. Recent research in this area has shown that the best clustering results can be achieved using multi-objective methods. In other words, assuming more than one criterion as objective functions for clustering data can measurably increase the quality of clustering. In this study, a model with two contradictory objective functions based on maximum data compactness in clusters (the degree of proximity of data) and maximum cluster separation (the degree of remoteness of clusters’ centers) is proposed. In order to solve this model, a recently proposed optimization method, the Multi-objective Improved Teaching Learning Based Optimization (MOITLBO) algorithm, is used. This algorithm is tested on several datasets and its clusters are compared with the results of some single-objective algorithms. Furthermore, with respect to noise, the comparison of the performance of the proposed model with another multi-objective model shows that it is robust to noisy data sets and thus can be efficiently used for multi-objective fuzzy clustering.

Multi-objective optimization for long tail recommendation

Recommender systems are tools to suggest items to target users. Accuracy-focused recommender systems tend to recommend popular items, while suggesting items with few ratings (long tail items) is also of great importance in practice. Recommending long tail items may cause an accuracy loss of recommendation results. Thus, it is necessary to have a recommendation framework that recommends unpopular items meanwhile minimizing the accuracy loss. In this paper, we formulate a multi-objective framework for long tail items recommendation. Under this framework, two contradictory objective functions are designed to describe the abilities of recommender system to recommend accurate and unpopular items, respectively. To optimize these two objective functions, a novel multi-objective evolutionary algorithm is proposed. This multi-objective evolutionary algorithm aims to find a set of tradeoff solutions by optimizing two objective functions simultaneously. Experiments show that the proposed framework is effective to suggest accurate and novel items. The proposed recommendation algorithm could suggest many high-quality recommendation lists for the target user based on the concept of Pareto dominance in one run.

Quality of Protection on WDM networks: A Recovery Probability based approach

WDM networks survivability needs a flexible quality of protection (QoP) due to the variety of existing connection demands, lack of fair resource distributions under traditional QoP and optimal resources assignment. Thus, this paper proposes a new paradigm of QoP based on generic levels of protection where the set of protection levels can be defined as the network administrator needs, i.e., a flexible QoP approach whose particular case is the traditional or non-flexible QoP approach. Essentially, the proposed generic level is based on the recovery probability concept which measures the grade of conflict among primary lightpaths that share backup lighpaths for link failure recovery. In order to study how this strategy impacts on the network cost, a Genetic Algorithm is proposed. It calculates the primary and backup lightpaths, considering a multi-objective optimization on the basis of lexicographical sorting approach. The Genetic Algorithm minimizes the number of blocked requests, the number of services without protection, the total differences between the requested QoP and the assigned QoP, and the network cost; all of which by considering the optical fibers used and subject to the wavelength usage as constraint. The experimental results indicate that the proposed approach –flexible QoP– is a promising strategy where the network cost, the number of requests and QoP levels are contradictory objective functions in environments with homogeneous and heterogeneous QoP requirements.

Multi Objective Optimization for Spur Gear Design Using Sheep Flocks Heredity Model Algorithm

In a perspective of stable industrial development to manufacture added consistent and economical industrial product, gears are ever more focus to requirements in terms of power capability, efficiency and compactness etc. In order to increase the performance factors of gears such as transmission capacity, efficiency, gear life, etc. is a difficult criteria for a design engineers as these are all progress in a conflicting behavior. This paper deals with the multi-objective optimization of spur gear drive design with two contradictory objective functions such as maximization of power transmission and minimization of volume of the gear drive. These objectives are approached by an optimization technique based on a Sheep Flocks Heredity Model Algorithm (SFHM) with design constraints like stress, center distance etc. A spur gear problem is solved with traditional trial method and results are compared with proposed algorithm.

A Multiobjective-driven Approach to Reduce Risk in Process Layouts

A multi-objective programming (MOP) approach is applied in this work to produce optimal facility layouts when some of the facilities may release toxic gas. A conflict appears when solving the optimization problem because reducing the occupied area increases the individual risk. In this approach, a utopia point is introduced as a reference, which is defined as the layout where no toxic releases are considered, i.e. the objective function consists on minimizing the total cost of interconnections and the occupied area. Then the Pareto set is built-up by incorporating individual risk to visualize the effect of the two contradictory objective functions. The closest point in the Pareto curve to the utopic reference is adopted here as the neutral risk point. A case-study is used to visualize risk-neutral, risk-averse and risk-seeking layouts where the overall stochastic individual risk is set up to a certain value. The optimization facility layout problem is formulated as a mixed-integer non-linear program (MINLP), which is highly non-convex.

Identification of multi-resolution network structures with multi-objective immune algorithm

Community structure is one of the most important properties in complex networks, and the field of community detection has received an enormous amount of attention in the past several years. Many quality metrics and methods have been proposed for revealing community structures at multiple resolution levels, while most existing methods need a tunable parameter in their quality metrics to determine the resolution level in advance. In this study, a multi-objective evolutionary algorithm (MOEA) for revealing multi-resolution community structures is proposed. The proposed MOEA-based community detection algorithm aims to find a set of tradeoff solutions which represent network partitions at different resolution levels in a single run. It adopts an efficient multi-objective immune algorithm to simultaneously optimize two contradictory objective functions, Modified Ratio Association and Ratio Cut. The optimization of Modified Ratio Association tends to divide a network into small communities, while the optimization of Ratio Cut tends to divide a network into large communities. The simultaneous optimization of these two contradictory objectives returns a set of tradeoff solutions between the two objectives. Each of these solutions corresponds to a network partition at one resolution level. Experiments on artificial and real-world networks show that the proposed method has the ability to reveal community structures of networks at different resolution levels in a single run.

Chaotic multi-objective optimization based design of fractional order PIλDμ controller in AVR system

In this paper, a fractional order (FO) PIλDμ controller is designed to take care of various contradictory objective functions for an automatic voltage regulator (AVR) system. An improved evolutionary non-dominated sorting genetic algorithm II (NSGA II), which is augmented with a chaotic map for greater effectiveness, is used for the multi-objective optimization problem. The Pareto fronts showing the trade-off between different design criteria are obtained for the PIλDμ and PID controller. A comparative analysis is done with respect to the standard PID controller to demonstrate the merits and demerits of the fractional order PIλDμ controller.