Gary B. Lamont

Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art

Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mid-eighties in an attempt to stochastically solve problems of this generic class. During the past decade, a variety of multiobjective EA (MOEA) techniques have been proposed and applied to many scientific and engineering applications. Our discussions intent is to rigorously define multiobjective optimization problems and certain related concepts, present an MOEA classification scheme, and evaluate the variety of contemporary MOEAs. Current MOEA theoretical developments are evaluated; specific topics addressed include fitness functions, Pareto ranking, niching, fitness sharing, mating restriction, and secondary populations. Since the development and application of MOEAs is a dynamic and rapidly growing activity, we focus on key analytical insights based upon critical MOEA evaluation of current research and applications. Recommended MOEA designs are presented, along with conclusions and recommendations for future work.

Applications of multi-objective evolutionary algorithms

An Introduction to Multi-Objective Evolutionary Algorithms and Their Applications Optimal Design of Industrial Electromagnetic Devices: A Multiobjective Evolutionary Approach Using a Particle Swarm Optimizer with a Multi-Objective Selection Scheme to Design Combinational Logic Circuits Automatic Control System Design via a Multiobjective Evolutionary Algorithm Evolutionary Multi-Objective Optimization of Trusses A Multi-Objective Evolutionary Algorithm for the Covering Tour Problem Multiobjective Aerodynamic Design and Visualization of Supersonic Wings by Using Adaptive Range Multiobjective Genetic Algorithms Mutli-Objective Spectroscopic Data Analysis of Inertial Confinement Fusion Implosion Cores: Plasma Gradient Determination On Machine Learning with Multiobjective Genetic Optimization and other papers.

An artificial immune system architecture for computer security applications

With increased global interconnectivity and reliance on e-commerce, network services and Internet communication, computer security has become a necessity. Organizations must protect their systems from intrusion and computer virus attacks. Such protection must detect anomalous patterns by exploiting known signatures while monitoring normal computer programs and network usage for abnormalities. Current anti-virus and network intrusion detection (ID) solutions can become overwhelmed by the burden of capturing and classifying new viral strains and intrusion patterns. To overcome this problem, a self-adaptive distributed agent-based defense immune system based on biological strategies is developed within a hierarchical layered architecture. A prototype interactive system is designed, implemented in Java and tested. The results validate the use of a distributed-agent biological system approach toward the computer security problems of virus elimination and ID.

On measuring multiobjective evolutionary algorithm performance

Solving optimization problems with multiple (often conflicting) objectives is generally a quite difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mid-eighties in an attempt to stochastically solve problems of this generic class. During the past decade a multiplicity of multiobjective EA (MOEA) techniques have been proposed and applied to many scientific and engineering applications. Our discussions intent is to rigorously define and execute a quantitative MOEA performance comparison methodology. Almost all comparisons cited in the current literature visually compare algorithmic results, resulting in only relative conclusions. Our methodology gives a basis for absolute conclusions regarding MOEA performance. Selected results from its execution with four MOEAs are presented and described.

Considerations in engineering parallel multiobjective evolutionary algorithms

Developing multiobjective evolutionary algorithms (MOEAs) involves thoroughly addressing the issues of efficiency and effectiveness. Once convinced of an MOEAs effectiveness the researcher often desires to reduce execution time and/or resource expenditure, which naturally leads to considering the MOEAs parallelization. Parallel MOEAs (pMOEAs) or distributed MOEAs are relatively new developments with few associated publications. pMOEA creation is not a simple task, involving analyzing various parallel paradigms and associated parameters. Thus, a thorough discussion of the major parallelized MOEA paradigms is included in this paper and succinct observations are made regarding an analysis of the current literature. Specifically, a previous MOEA notation is extended into the pMOEA domain to enable precise description and identification of various sets of interest. Innovative concepts for pMOEA migration, replacement and niching schemes are discussed, as well as presenting the first known generic pMOEA formulation. Taken together, this papers analyses in conjunction with an original pMOEA design serve as a pedagogical framework and example of the necessary process to implement an efficient and effective pMOEA.

Multiobjective evolutionary algorithm test suites

Multiobjective Evolutionary Algorithms (MOEAs) currently have no generic benchmark test suites. This paper provides several Multiobjective Optimization Problems (MOPS) for use as part of a standardized MOEA test suite, and proposes a methodology whereby various MOEAs can be directly compared. Supporting these contributions is a detailed discussion of MOP landscape and general test suite issues, and presentation of a new theorem defining the structural limitations of an MOP’s global optimum. This paper also discusses high-performance computer software deterministically computing an MOP’s Pareto front at a given computational resolution.

Visualizing particle swarm optimization - Gaussian particle swarm optimization

Particle swarm optimization (PSO) conjures an image of particles searching for the optima the way bees buzz around flowers. One approach at visualizing the swarm graphs where all the particles are each generation, thus demonstrating the random nature associated with swarms of insects. Another approach is to show successive bests, thus showing the way that the swarm progresses. Some have even looked at the specific search path of the particle that eventually finds the optima. These approaches provide limited understanding of PSO. This paper presents a new visualization approach based on the probability distribution of the swarm, thus the random nature of PSO is properly visualized. The visualization allows better understanding of how to tune the algorithm and depicts weaknesses. A new algorithm based on moving the swarm a Gaussian distance from the global and local best is presented. Gaussian particle swarm optimization (GPSO) is compared to PSO.

Load balancing for heterogeneous clusters of PCs

Abstract With commercial supercomputers and homogeneous clusters of PCs, static load balancing is accomplished by assigning equal tasks to each processor. With heterogeneous clusters, the system designers have the option of quickly adding newer hardware that is more powerful than the existing hardware. When this is done, the assignment of equal tasks to each processor results in suboptimal performance. This research addresses techniques by which the size of the task assigned to a processor is a suitable match. Thus, the more powerful processors do more work and the less powerful processors perform less work. We find that when the range of processing power is narrow, some benefit can be achieved with asymmetric load balancing. When the range of processing power is broad, dramatic improvements in performance are realized — our experiments have shown up to 92% improvement when asymmetrically load balancing a modified version of the computationally intensive NAS Parallel Benchmarks’ LU application on a heterogeneous cluster of Linux-powered PCs.

FastPGA: a dynamic population sizing approach for solving expensive multiobjective optimization problems

We present a new multiobjective evolutionary algorithm (MOEA), called fast Pareto genetic algorithm (FastPGA). FastPGA uses a new fitness assignment and ranking strategy for the simultaneous optimization of multiple objectives where each solution evaluation is computationally- and/or financially-expensive. This is often the case when there are time or resource constraints involved in finding a solution. A population regulation operator is introduced to dynamically adapt the population size as needed up to a user-specified maximum population size. Computational results for a number of well-known test problems indicate that FastPGA is a promising approach. FastPGA outperforms the improved nondominated sorting genetic algorithm (NSGA-II) within a relatively small number of solution evaluations.

A particle swarm model for swarm-based networked sensor systems

Swarm behavior as demonstrated by flocks of birds, schools of fish, and swarms of insects provide a useful method for implementing a distributed network of mobile sensor platforms. Such mobile sensor swarm systems are useful for various search or surveillance activities. Swarm behavior ensures safe separation between swarm members while enforcing a level of cohesion. These two properties, when considered in the context of sensors and wireless communications, provide for low redundancy coverage and a robust and reliable communications system. This paper examines particle swarm behavior through simulation with respect to such a sensor network. Analysis of swarm behavior for various parameter settings indicate a classification methodology. This provides a foundation for a proposed taxonomy.