Attributed multi-objective comprehensive learning particle swarm optimization for optimal security of networks

In particle swarm optimization (PSO) each particle uses its personal and global or local best positions by linear summation. However, it is very time consuming to find the global or local best positions in case of complex problems. To overcome this problem, we propose a new multi-objective variant of PSO called attributed multi-objective comprehensive learning particle swarm optimizer (A-MOCLPSO). In this technique, we do not use global or local best positions to modify the velocity of a particle; instead, we use the best position of a randomly selected particle from the whole population to update the velocity of each dimension. This method not only increases the speed of the algorithm but also searches in more promising areas of the search space. We perform an extensive experimentation on well-known benchmark problems such as Schaffer (SCH), Kursawa (KUR), and Zitzler–Deb–Thiele (ZDT) functions. The experiments show very convincing results when the proposed technique is compared with existing versions of PSO known as multi-objective comprehensive learning particle swarm optimizer (MOCLPSO) and multi-objective particle swarm optimization (MOPSO), as well as non-dominated sorting genetic algorithm II (NSGA-II). As a case study, we apply our proposed A-MOCLPSO algorithm on an attack tree model for the security hardening problem of a networked system in order to optimize the total security cost and the residual damage, and provide diverse solutions for the problem. The results of our experiments show that the proposed algorithm outperforms the previous solutions obtained for the security hardening problem using NSGA-II, as well as MOCLPSO for the same problem. Hence, the proposed algorithm can be considered as a strong alternative to solve multi-objective optimization problems.     

The effects of asymmetric neighborhood assignment in the MOEA/D algorithm

The Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D) is a very efficient multiobjective evolutionary algorithm introduced in recent years. This algorithm works by decomposing a multiobjective optimization problem to many scalar optimization problems and by assigning each specimen in the population to a specific subproblem. The MOEA/D algorithm transfers information between specimens assigned to the subproblems using a neighborhood relation.In this paper it is shown that parameter settings commonly used in the literature cause an asymmetric neighbor assignment which in turn affects the selective pressure and consequently causes the population to converge asymmetrically. The paper contains theoretical explanation of how this bias is caused as well as an experimental verification. The described effect is undesirable, because a multiobjective optimizer should not introduce asymmetries not present in the optimization problem. The paper gives some guidelines on how to avoid such artificial asymmetries.     

Modeling a mixed-integer-binary small-population evolutionary particle swarm algorithm for solving the optimal power flow problem in electric power systems

This research discusses the application of a mixed-integer-binary small-population-based evolutionary particle swarm optimization to the problem of optimal power flow, where the optimization problem has been formulated taking into account four decision variables simultaneously: active power (continuous), voltage generator (continuous), tap position on transformers (integer) and shunt devices (binary). The constraint handling technique used in the algorithm is based on a strategy to generate and keep the decision variables in feasible space through the heuristic operators. The heuristic operators are applied in the active power stage and the reactive power stage sequentially. Firstly, the heuristic operator for the power balance is computed in order to maintain the power balance constraint through a re-dispatch of the thermal units. Secondly, the heuristic operators for the limit of active power flows and the bus voltage constraint at each generator bus are executed through the sensitivity factors. The advantage of our approach is that the algorithm focuses the search of the decision variables on the feasible solution space, obtaining a better cost in the objective function. Such operators not only improve the quality of the final solutions but also significantly improve the convergence of the search process. The methodology is verified in several electric power systems.     

Artificial bee colony algorithm solution for optimal reactive power flow

Artificial bee colony (ABC) algorithm is an optimization algorithm based on the intelligent foraging behavior of honeybee swarm. Optimal reactive power flow (ORPF) based on ABC algorithm to minimize active power loss in power systems is studied in this paper. The advantage of ABC algorithm is that it does not require these parameters, because it is very difficult to determine external parameters such as cross over rate and mutation rate as in case of genetic algorithm and differential evolution. The other advantage is that global search ability of the algorithm is implemented by introducing a neighborhood source production mechanism which is similar to mutation process. Because of these features, ABC algorithm attracts much attention in recent years and has been used successfully in many areas. ORPF problem is one of these areas. In this paper, proposed algorithm is tested on both standard IEEE 30-bus test system and IEEE 118-bus test system. To show the effectiveness of proposed algorithms, the obtained results are compared with different approaches as available in the literature.     

A multi-objective evolutionary approach for generator scheduling

This paper presents a novel, two-phase approach for optimal generation scheduling, taking into account the environmental issue of emission allowance trading in addition to the economic issue of operation cost. In the first phase, hourly-optimal scheduling is done to simultaneously minimize operation cost, emission, and transmission loss, while satisfying constraints such as power balance, spinning reserve and power generation limits. In the second phase, the minimum up/down time and ramp up/down rate constraints are considered, and a set of 24-h optimal schedules is obtained using the outputs of the first phase. Simulation results indicate effectiveness of the proposed approach.     

A dynamic multi-objective approach for the reconfigurable multi-facility layout problem

The multi-facility layout problem involves the physical organization of departments inside several facilities, to allow flexible and efficient operations. This work studies the facility layout problem in a new perspective, considering a group of facilities, and two different concerns: the location of departments within a group of facilities, and the location of departments inside each facility itself. The problem is formulated as a Quadratic Programming Problem with multiple objectives and unequal areas, allowing layout reconfigurations in each planning period. The objectives of the model are: the minimization of costs (material handling inside facilities and between facilities, and re-layout); the maximization of adjacency between departments; and the minimization of the “unsuitability” of department positions and locations. This unsuitability measure is a new objective proposed in this work, to combine the characteristics of existing locations with the requirements of departments. The model was tested with data from the literature as well as with a problem inspired in a first tier supplier in the automotive industry. Preliminary results show that this work can be viewed as an innovative and promising integrated approach for tackling real, complex facility layout problems.     

Differential evolution approach for optimal reactive power dispatch

Differential evolution based optimal reactive power dispatch for real power loss minimization in power system is presented in this paper. The proposed methodology determines control variable settings such as generator terminal voltages, tap positions and the number of shunts to be switched, for real power loss minimization in the transmission system. The problem is formulated as a mixed integer nonlinear optimization problem. A generic penalty function method, which does not require any penalty coefficient, is employed for constraint handling. The formulation also checks for the feasibility of the optimal control variable setting from a voltage security point of view by using a voltage collapse proximity indicator. The algorithm is tested on standard IEEE 14, IEEE 30, and IEEE 118-Bus test systems. To show the effectiveness of proposed method the results are compared with Particle Swarm Optimization and a conventional optimization technique – Sequential Quadratic Programming.     

A gradient flow approach to decentralised output feedback optimal control

This paper visits the quadratic optimal control problem of decentralised control systems via static output feedback. A gradient flow approach is introduced as a tool to compute the optimal output feedback gain. Several nice properties are revealed concerning the convergence of the gain matrix along the trajectory of an ordinary differential equation obtained from the gradient of objective cost, i.e. the objective cost is decreasing along this trajectory. If the equilibrium points are isolated, the convergence can be guaranteed. A simulation example is given to illustrate the effectiveness of this approach.     

A hybrid multi-objective immune algorithm for a flow shop scheduling problem with bi-objectives: Weighted mean completion time and weighted mean tardiness

This paper investigates a novel multi-objective model for a no-wait flow shop scheduling problem that minimizes both the weighted mean completion time and weighted mean tardiness . Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. This paper presents a new hybrid multi-objective algorithm based on the features of a biological immune system (IS) and bacterial optimization (BO) to find Pareto optimal solutions for the given problem. To validate the performance of the proposed hybrid multi-objective immune algorithm (HMOIA) in terms of solution quality and diversity level, various test problems are examined. Further, the efficiency of the proposed algorithm, based on various metrics, is compared against five prominent multi-objective evolutionary algorithms: PS-NC GA, NSGA-II, SPEA-II, MOIA, and MISA. Our computational results suggest that our proposed HMOIA outperforms the five foregoing algorithms, especially for large-sized problems.     

Application of modified NSGA-II algorithm to multi-objective reactive power planning

This paper discusses the application of Modified Non-Dominated Sorting Genetic Algorithm-II (MNSGA-II) to multi-objective Reactive Power Planning (RPP) problem. The three objectives considered are minimization of combined operating and VAR allocation cost, bus voltage profile improvement and voltage stability enhancement. For maintaining good diversity in nondominated solutions, Dynamic Crowding Distance (DCD) procedure is implemented in NSGA-II and it is called as MNSGA-II. The standard IEEE 30-bus test system, practical 69-bus Indian system and IEEE 118-bus system are considered to analyze the performance of MNSGA-II. The results obtained using MNSGA-II are compared with NSGA-II and validated with reference pareto-front generated by conventional weighted sum method using Covariance Matrix Adapted Evolution Strategy (CMA-ES). The performance of NSGA-II and MNSGA-II are compared with respect to best, mean, worst and standard deviation of multi-objective performance measures namely gamma, spread, minimum spacing and Inverted Generational Distance (IGD) in 15 independent runs. The results show the effectiveness of MNSGA-II and confirm its potential to solve the multi-objective RPP problem. A decision-making procedure based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used for finding best compromise solution from the set of pareto-solutions obtained through MNSGA-II.     

A multi-objective approach to facility layout problem by genetic search algorithm and Electre method

Classical approaches to layout design problem tend to maximise the efficiency of layout, measured by the handling cost related to the interdepartmental flow and to the distance among the departments. However, the actual problem involves several conflicting objectives hence requiring a multi-objective formulation. Multi-objective approaches, recently proposed, in most cases lead to the maximisation of a weighted sum of score functions. The poor practicability of such an approach is due to the difficulty of normalising these functions and of quantifying the weights. In this paper, this difficulty is overcome by approaching the problem in two subsequent steps: in the first step, the Pareto-optimal solutions are determined by employing a multi-objective constrained genetic algorithm and the subsequent selection of the optimal solution is carried out by means of the multi-criteria decision-making procedure Electre. This procedure allows the decision maker to express his preferences on the basis of the knowledge of candidate solution set. Quantitative (handling cost) and qualitative (adjacency and distance requests between departments) objectives are considered referring to a bay structure-based layout model, that allows to take into account also practical constraints such as the aspect ratio of departments. Results obtained confirm the effectiveness of the proposed procedure as a practicable support tool for layout designers.     

Optimal power flow using an Improved Colliding Bodies Optimization algorithm

This paper proposes Improved Colliding Bodies Optimization (ICBO) algorithm to solve efficiently the optimal power flow (OPF) problem. Several objectives, constraints and formulations at normal and preventive operating conditions are used to model the OPF problem. Applications are carried out on three IEEE standard test systems through 16 case studies to assess the efficiency and the robustness of the developed ICBO algorithm. A proposed performance evaluation procedure is proposed to measure the strength and robustness of the proposed ICBO against numerous optimization algorithms. Moreover, a new comparison approach is developed to compare the ICBO with the standard CBO and other well-known algorithms. The obtained results demonstrate the potential of the developed algorithm to solve efficiently different OPF problems compared to the reported optimization algorithms in the literature.     

A genetic algorithm approach for multi-objective optimization of supply chain networks

Supply chain network (SCN) design is to provide an optimal platform for efficient and effective supply chain management. It is an important and strategic operations management problem in supply chain management, and usually involves multiple and conflicting objectives such as cost, service level, resource utilization, etc. This paper proposes a new solution procedure based on genetic algorithms to find the set of Pareto-optimal solutions for multi-objective SCN design problem. To deal with multi-objective and enable the decision maker for evaluating a greater number of alternative solutions, two different weight approaches are implemented in the proposed solution procedure. An experimental study using actual data from a company, which is a producer of plastic products in Turkey, is carried out into two stages. While the effects of weight approaches on the performance of proposed solution procedure are investigated in the first stage, the proposed solution procedure and simulated annealing are compared according to quality of Pareto-optimal solutions in the second stage.     

A multi-objective dynamic vehicle routing problem with fuzzy time windows: Model,solution and application

In this paper, a multi-objective dynamic vehicle routing problem with fuzzy time windows (DVRPFTW) is presented. In this problem, unlike most of the work where all the data are known in advance, a set of real time requests arrives randomly over time and the dispatcher does not have any deterministic or probabilistic information on the location and size of them until they arrive. Moreover, this model involves routing vehicles according to customer-specific time windows, which are highly relevant to the customers’ satisfaction level. This preference information of customers can be represented as a convex fuzzy number with respect to the satisfaction for a service time. This paper uses a direct interpretation of the DVRPFTW as a multi-objective problem where the total required fleet size, overall total traveling distance and waiting time imposed on vehicles are minimized and the overall customers’ preferences for service is maximized. A solving strategy based on the genetic algorithm (GA) and three basic modules are proposed, in which the state of the system including information of vehicles and customers is checked in a management module each time. The strategy module tries to organize the information reported by the management module and construct an efficient structure for solving in the subsequent module. The performance of the proposed approach is evaluated in different steps on various test problems generalized from a set of static instances in the literature. In the first step, the performance of the proposed approach is checked in static conditions and then the other assumptions and developments are added gradually and changes are examined. The computational experiments on data sets illustrate the efficiency and effectiveness of the proposed approach.     

Solving a dial-a-ride problem with a hybrid evolutionary multi-objective approach: Application to demand responsive transport

Demand responsive transport allows customers to be carried to their destination as with a taxi service, provided that the customers are grouped in the same vehicles in order to reduce operational costs. This kind of service is related to the dial-a-ride problem. However, in order to improve the quality of service, demand responsive transport needs more flexibility. This paper tries to address this issue by proposing an original evolutionary approach. In order to propose a set of compromise solutions to the decision-maker, this approach optimizes three objectives concurrently. Moreover, in order to intensify the search process, this multi-objective evolutionary approach is hybridized with a local search. Results obtained on random and realistic problems are detailed to compare three state-of-the-art algorithms and discussed from an operational point of view.     

MOEA/D + uniform design: A new version of MOEA/D for optimization problems with many objectives

To extend multiobjective evolutionary algorithm based on decomposition (MOEA/D) in higher dimensional objective spaces, this paper proposes a new version of MOEA/D with uniform design, named the uniform design multiobjective evolutionary algorithm based on decomposition (UMOEA/D), and compares the proposed algorithm with MOEA/D and NSGA-II on some scalable test problems with three to five objectives. UMOEA/D adopts the uniform design method to set the aggregation coefficient vectors of the subproblems. Compared with MOEA/D, distribution of the coefficient vectors is more uniform over the design space, and the population size neither increases nonlinearly with the number of objectives nor considers a formulaic setting. The experimental results indicate that UMOEA/D outperforms MOEA/D and NSGA-II on almost all these many-objective test instances, especially on problems with higher dimensional objectives and complicated Pareto set shapes. Experimental results also show that UMOEA/D runs faster than NSGA-II for the problems used in this paper. In additional, the results obtained are very competitive when comparing UMOEA/D with some other algorithm on the multiobjective knapsack problems.     

A multi-objective nonlinear optimization approach to designing effective air quality control policies

This paper presents the implementation of a two-objective optimization methodology to select effective tropospheric ozone pollution control strategies on a mesoscale domain. The objectives considered are (a) the emission reduction cost and (b) the Air Quality Index. The control variables are the precursor emission reductions due to available technologies. The nonlinear relationship linking air quality objective and precursor emissions is described by artificial neural networks, identified by processing deterministic Chemical Transport Modeling system simulations. Pareto optimal solutions are calculated with the Weighted Sum Strategy. The two-objective problem has been applied to a complex domain in Northern Italy, including the Milan metropolitan area, a region characterized by frequent and persistent ozone episodes.     

一种单电源供电的十位双积分A/D转换器

设计了一种改进的双积分A/D转换器,通过采取2种模式积分的方法实现了+5V单电源供电,克服了传统双积分A/D转换器需要双电源的弊端。重点考虑可处理输入信号范围以及功耗的优化,并使用栅压自举开关保证了A/D转换的精度。整体电路设计基于CSMC0.5μm 2P3M CMOS工艺,使用Cadence Spectre进行仿真,在5V供电情况下达到10bit精度,转换速率>10Kb/s,输入电压范围达到0~5V,INL<1/2LSB,系统功耗2.636mW。     

A hybrid multi-objective approach based on the genetic algorithm and neural network to design an incremental cellular manufacturing system

One important issue related to the implementation of cellular manufacturing systems (CMSs) is to decide whether to convert an existing job shop into a CMS comprehensively in a single run, or in stages incrementally by forming cells one after the other, taking the advantage of the experiences of implementation. This paper presents a new multi-objective nonlinear programming model in a dynamic environment. Furthermore, a novel hybrid multi-objective approach based on the genetic algorithm and artificial neural network is proposed to solve the presented model. From the computational analyses, the proposed algorithm is found much more efficient than the fast non-dominated sorting genetic algorithm (NSGA-II) in generating Pareto optimal fronts.     

A multi-objective approach to optimal placement and sizing of multiple active power filters using a music-inspired algorithm

This paper proposes a new multi-objective framework for optimal placement and sizing of the active power filters (APFs) with satisfactory and acceptable standard levels. total harmonic distortion (THD) of voltage, harmonic transmission line loss (HTLL), motor load loss function (MLLF), and total APFs currents are the four objectives considered in the optimization, while harmonic distortions within standard level, and maximum allowable APF size, are modeled as constraints. The proposed model is one of non-convex optimization problem having a non-linear, mixed-integer nature. Since, a new modified harmony search algorithm (MHSA) is used and followed by a min–max technique in order to obtain the final optimal solution. The harmony search algorithm is a recently developed optimization algorithm, which imitates the music improvisation process. In this process, the Harmonists improvise their instrument pitches searching for the perfect state of harmony. The newly developed method has been applied on the IEEE 18-bus test system and IEEE 30-bus test system by different scenarios and cases to demonstrate the feasibility and effectiveness of the proposed method. The detailed results of the case studies are presented and thoroughly analyzed. The obtained results illustrate the sufficiency and profitableness of the newly developed method in the placement and sizing of the multiple active power filters, when compared with other methods.