Weighted Sum Genetic Algorithm Research Articles

Multidisciplinary Design Optimization of Active Debris Removal Mission via Electric Propulsion

A design optimization framework for an active orbital debris removal mission to minimize the life cycle cost and mission time is presented. The general concept of operations consists of spacecraft capable of deorbiting 22 identical high-priority debris from sun-synchronous orbit via continuous low thrust from electrostatic thrusters. This modular subsystem framework is proposed and investigated with a fractional factorial design of experiments approach as preliminary analysis in order to inform effects of design variables as well as identify the scope of possible design and output space. The single-objective optimization minimizes the life cycle cost of the mission through a heuristic-based genetic algorithm, resulting in a 4.12-year, $354 million mission using 5.34 kW xenon Hall thrusters. Pareto fronts generated by a multiobjective weighted-sum genetic algorithm are presented to highlight the cost and time objective space for both a nominal and expensive xenon price per kilogram to show design space fluctuations with xenon price volatility. High-power 8 kW xenon Hall thrusters are proven to be in closest proximity to the utopia of the multiobjective design space with a cost of $687 million and mission time of 1 year. High-power krypton Hall thrusters prove to offer a comparable alternative to xenon systems.

Evaluation and efficiency comparison of evolutionary algorithms for service placement optimization in fog architectures

This study compares three evolutionary algorithms for the problem of fog service placement: weighted sum genetic algorithm (WSGA), non-dominated sorting genetic algorithm II (NSGA-II), and multiobjective evolutionary algorithm based on decomposition (MOEA/D). A model for the problem domain (fog architecture and fog applications) and for the optimization (objective functions and solutions) is presented. Our main concerns are related to optimize the network latency, the service spread and the use of the resources. The algorithms are evaluated with a random Barabasi–Albert network topology with 100 devices and with two experiment sizes of 100 and 200 application services. The results showed that NSGA-II obtained the highest optimizations of the objectives and the highest diversity of the solution space. On the contrary, MOEA/D was better to reduce the execution times. The WSGA algorithm did not show any benefit with regard to the other two algorithms.

Multi-objective uncapacitated facility location problem with customers’ preferences: Pareto-based and weighted sum GA-based approaches

The uncapacitated facility location problem (UFLP) is a well-known combinatorial optimization problem having single-objective function. The objective of UFLP is to find a subset of facilities from a given set of potential facility locations such that the sum of the opening costs of the opened facilities and the service cost to serve all the customers is minimized. In traditional UFLP, customers are served by their nearest facilities. In this article, we have proposed a multi-objective UFLP where each customer has a preference for each facility. Hence, the objective of the multi-objective UFLP with customers’ preferences (MOUFLPCP) is to open a subset of facilities to serve all the customers such that the sum of the opening cost and service cost is minimized and the sum of the preferences is maximized. In this article, the elitist non-dominated sorting genetic algorithm II (NSGA-II), a popular Pareto-based GA, is employed to solve this problem. Moreover, a weighted sum genetic algorithm (WSGA)-based approach is proposed to solve MOUFLPCP where conflicting two objectives of the problem are aggregated to a single quality measure. For experimental purposes, new test instances of MOUFLPCP are created from the existing UFLP benchmark instances and the experimental results obtained using NSGA-II and WSGA-based approaches are demonstrated and compared for these newly created test instances.

Different Methods of Artificial Intelligence Used for Optimization the Turning Process

In this paper, we realize a comparative study between some heuristics methods applied in turning operation in order to find optimal cutting parameters. We consider five different constraints aimed to achieve minimum total cost of machining. We have chosen the Simulated Annealing (SA) – a local search method, and Weighted-Sum Genetic Algorithm (WSGA) – a non-Pareto approach of a multi-objective optimization algorithm, based on a weighted aggregation of objectives. The aggregation may be with fixed weights or with random (variable) weights. The simulations showed that, even if it produces better results than the SA, WSGA with fixed weights, does not lead to optimum results, highlighting in this way that in the formula of the cost function, some cost components may be more important than others. In addition, we extend our previous work by integrating in the software application a new optimization method: WSGA with random weights. Also, we increase the application’s flexibility by reconfiguring the graphical user interface in order to allow tuning the optimization techniques parameters.

Implementing some Evolutionary Computing Methods for Determining the Optimal Parameters in the Turning Process

In this paper, we comparatively present two heuristics search methods – Simulated Annealing and Weighted Sum Genetic Algorithm, in order to find optimal cutting parameters in turning operation. We consider five different constraints aiming to achieve minimum total cost of machining. We developed a customizable software application in Microsoft Visual Studio with C# source code, flexible and extensible that implements the optimization methods. The experiments are based on real data gathered from S.C. “Compa” S.A Sibiu, a company that manufactures automotive components and targets improving of product quality and reducing cost and production time. The obtained results show that, although the Weighted Sum Genetic Algorithm does not guarantee the optimality of finals solution despite of a high probability to be, it is superior to that provided by Simulated Annealing.

A modified tone injection scheme for PAPR reduction using genetic algorithm

Abstract Owing to the capability to provide a wide variety of intelligent behaviors, cognitive radio (CR) has become a promising technology to improve spectrum utilization efficiently. One of the popular techniques which adapt CR concept to multi-carrier systems is known as tone injection scheme. This scheme is a sort of peak-to-average-power ratio (PAPR) reduction methods deployable to multi-carrier systems such as orthogonal frequency division multiplexing (OFDM). However, a conventional tone injection scheme might increase averaged transmit power attributed to expanding the size of constellation on purpose to get optimal PAPR reduction. Based on a weighted-sum genetic algorithm to resolve multi-objective optimization problem (MOOP), the modified tone injection scheme exploits the agility of CR technology to rapidly adapt operating parameters in order to fulfill PAPR reduction as well as mitigation of power increase optimally. The simulation results verify that the proposed scheme is flexible because it could not only control the performance of PAPR reduction, but also alleviate power increase by steering weight values at the expense of relatively low complexity comparing with other conventional method.

Optimization of Electrical Energy Consumption and Level Reliability of Water Supply System

Generally, high operational cost is associated with all water supply system. This is as a result of the high amount of electric energy consumption ascribed to the system due to its components. The water supply system of the Mara-Japan Industrial Institute (MJII), Beranang, Selangor is one of such system that suffers this challenge of high operational cost. In this paper we have applied the use of an Adaptive Weighted Sum Genetic Algorithm to optimize the system operations such that it minimizes the high energy consumption as well as ensuring the overall reliability of the water level in the reservoir. The results obtained from the optimized model of the system show a promising and a significant reduction to the tune of 34.97% in the amount of energy consumed as compared with that of normal operations.

Multi-objective approach for load shedding based on voltage stability index consideration

In voltage stability analysis, it is useful to assess voltage stability of power systems by means of scalar magnitudes, or indices. Operators can use voltage stability indices to know how close the system to voltage collapse. The voltage stability indices are a powerful tool to identify the weakest bus and critical line. This identification can be used to gain control over devices for voltage stability up to certain level and load shedding is possible if the load keeps on increasing. This paper presents a computationally simple index based load shedding algorithm using weighted sum genetic algorithm where an AC power flow solution cannot be found for the stressed conditions. Minimization of total load shed and sum of New Voltage Stability Index (NVSI) at the selected buses are considered as two objectives of this algorithm to restore the power flow solvability. This is validated in both IEEE 30 bus system and a practical system Tamil Nadu Electricity board (TNEB) 69 bus system in India for considering both heavy loading and (N−1) contingency.

ECM Parameters Modeling and Optimization Using WSGA

Electrochemical machining (ECM) is a non tradition process used for the machining of metal matrix composites. Metal matrix composites are used for applications in aero scope, automobile industries and medical field. Determination of optimal process parameter is difficult in ECM machining process for obtaining maximum Material Removal Rate (MRR) and good Surface Roughness (SR).The multiple regression model was used to obtain the relationship between process parameters and output parameters and Weighted Sum Genetic Algorithm (WSGA) optimization was proposed to optimize the ECM process parameter. .The Voltage, Current, Feed Rate and Electrolyte Concentration are considered as decision variables, MRR and SR are the machining parameters used in the proposed work.

Simulation-based ATPG for low power testing of crosstalk delay faults in asynchronous circuits

A new multi–objective genetic algorithm has been proposed for testing crosstalk delay faults in asynchronous sequential circuits that reduces average power dissipation during test application. The proposed Elitist Non–dominated Sorting Genetic Algorithm (ENGA)–based Automatic Test Pattern Generation (ATPG) for crosstalk induced delay faults generates test pattern set that has high fault coverage and low switching activity. Redundancy is introduced in ENGA–based ATPG by modifying the fault dropping phase and hence a very good reduction in transition activity is achieved. Tests are generated for several asynchronous SIS benchmark circuits. Experimental results demonstrate that ENGA gives higher fault coverage, reduced transitions and compact test vectors for most of the asynchronous benchmark circuits when compared with those generated by Weighted Sum Genetic Algorithm (WSGA).

Tree structured encoding based multi-objective multicast routing algorithm

Quality-of-service (QoS) based multicast routing is a major challenge to next generation networks due to the increasing demand of real-time applications which require strict QoS guarantee. In the presented multi-objective multicast routing, the QoS parameters, namely, cost and available bandwidth are represented as objectives, while end-to-end delay and delay jitter are represented as constraints. The optimization is strived using an elitist multi-objective evolutionary algorithm. The topological assisted tree structured encoding was proposed to represent the multicast tree. The individual solution or chromosome was represented as a combination of arrays where each array represents a random route from destination node in multicast group to source node. The effectiveness of the proposed algorithm is tested on various networks, including the network formed using network topology generator BRITE. The best compromise solution is obtained using fuzzy cardinal priority ranking. The performance of this algorithm was compared with weighted sum genetic algorithm. The simulation results demonstrate that the multi-objective optimization with the proposed encoding scheme is effective in providing faster and guaranteed convergence. Key words: Multicast routing, multi-objective optimization, tree structured encoding, evolutionary algorithm, genetic algorithm.

Simulation Based Low Powertest Generation for Crosstalk Delay Faults

Crosstalk faults in deep submicron cause severe design validation and test problems. Further power consumption during testing is becoming a critical factor. The proposed Weighted Sum Genetic Algorithm (WSGA) based Automatic Test Pattern Generation (ATPG) for crosstalk induced delay faults generates test sequences that has high-fault coverage with reduced power consumption. The effectiveness of various crossover operators was analysed. Experimental results with ISCAS 85 and scan version of ISCAS 89 benchmark circuits demonstrate that the proposed WSGA based ATPG with Weight based Crossover Operator (WCO) provides compact test vectors that have higher fault coverage with minimum number of transitions for most of the benchmark circuits when compared with those generated by Genetic Algorithm (GA) based ATPG.

A FAST AND ELITIST BI-OBJECTIVE EVOLUTIONARY ALGORITHM FOR SCHEDULING INDEPENDENT TASKS ON HETEROGENEOUS SYSTEMS

To meet the increasing computational demands, geographically distributed resources need to be logically coupled to make them work as a unified resource. In analyzing the performance of such distributed heterogeneous computing systems scheduling a set of tasks to the available set of resources for execution is highly important. Task scheduling being an NP-complete problem, use of metaheuristics is more appropriate in obtaining optimal solutions. Schedules thus obtained can be evaluated using several criteria that may conflict with one another which require multi objective problem formulation. This paper investigates the application of an elitist Nondominated Sorting Genetic Algorithm (NSGA-II), to efficiently schedule a set of independent tasks in a heterogeneous distributed computing system. The objectives considered in this paper include minimizing makespan and average flowtime simultaneously. The implementation of NSGA-II algorithm and Weighted-Sum Genetic Algorithm (WSGA) has been tested on benchmark instances for distributed heterogeneous systems. As NSGA-II generates a set of Pareto optimal solutions, to verify the effectiveness of NSGA-II over WSGA a fuzzy based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto solution set.

Efficient multi-objective genetic algorithm for hardware-software partitioning in embedded system design: ENGA

This paper presents a novel multi-objective evolutionary algorithm for hardware software partitioning of embedded systems. Customised genetic algorithms have been effectively used for solving complex optimisation problems (NP Hard) but are mainly applied to optimise a particular solution with respect to a single objective. Many real world problems in embedded systems have multiple objective functions like area, performance, power, latency, etc., which are to be maximised or minimised at the early stage of the design process. Hence multi-objective formulations are realistic models for many complex engineering optimisation problems. A multi-objective optimisation problem usually has a set of Pareto-optimal solutions, instead of one single optimal solution. A method is put forward for generating Pareto solutions using elitist non-dominated sorting genetic algorithm (ENGA) whose complexity is only O(MN²), where M is the number of objectives and N is the population size. The algorithm is implemented using Visual C++ and the performance metrics for weighted-sum genetic algorithm (WSGA) and ENGA are compared. The results of extensive hardware/software partitioning technique on numerous benchmarks are also presented which can be used practically at the early stage of the design process. From the simulation results ENGA (NSGA-II) was found to perform better than WSGA.

Selection and Designing of Command Shaper for Vibration Control of Flexible Manipulators: A Multi-objective Optimization Approach

This paper presents a selection and design scheme of multimodal command shapers using the multi-objective genetic algorithm (MOGA). A control scheme comprised of a command shaper and a collocated proportionalderivative control is employed in order to reduce end-point vibration without sacrificing the system’s response speed. Command shaping causes a delay in the system’s response and, also, reduces system vibration and, in this manner, the amount of vibration reduction and the response delay conflict with each other. Conventional methods can hardly provide a solution that satisfies several design objectives demanded by practical applications due to the competing nature of these objectives. Furthermore, the selection of a shaping technique is crucial since robustness and computational complexity depend on the shaping technique. This paper proposes a combined approach to selecting and designing command shapers using MOGA. A comparative assessment of the performance of the proposed approach with the conventional single-objective and weighted-sum genetic algorithm optimization approaches is also provided. The proposed technique can provide a wide range of solutions in a single run to conflicting design objectives and satisfy associated goals.

A weighted sum genetic algorithm to support multiple-party multiple-objective negotiations

Negotiations are a special class of group decision-making problems that can be formulated as constrained optimization problems and are characterized by high degrees of conflict among the negotiation participants. A variety of negotiation support techniques have been used to help find solutions acceptable to all parties in a negotiation. The paper presents an approach that employs a genetic algorithm (GA) for finding acceptable solutions for multiparty multiobjective negotiations. The GA approach is consistent with the complex nature of real-world negotiations and is therefore capable of addressing more realistic negotiation scenarios than previous techniques in the literature allow. In addition to the traditional genetic operators of reproduction, crossover, and mutation, the search is enhanced with a new operator called trade. The trade operator simulates concessions that might be made by parties during the negotiation process. GA performance with the trade operator is compared to a traditional GA, nonlinear programming, a hill-climber, and a random search. Experimental results show the GA with the trade operator performs better than these other more traditional approaches.