Evolutionary algorithms (EAs) are often well-suited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a single run. However, the few comparative studies of different methods presented up to now remain mostly qualitative and are often restricted to a few approaches. In this paper, four multiobjective EAs are compared quantitatively where an extended 0/1 knapsack problem is taken as a basis. Furthermore, we introduce a new evolutionary approach to multicriteria optimization, the strength Pareto EA (SPEA), that combines several features of previous multiobjective EAs in a unique manner. It is characterized by (a) storing nondominated solutions externally in a second, continuously updated population, (b) evaluating an individual's fitness dependent on the number of external nondominated points that dominate it, (c) preserving population diversity using the Pareto dominance relationship, and (d) incorporating a clustering procedure in order to reduce the nondominated set without destroying its characteristics. The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface. Moreover, SPEA clearly outperforms the other four multiobjective EAs on the 0/1 knapsack problem.

Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach

The paradigmatic shift from a Web of manual interactions to a Web of programmatic interactions driven by Web services is creating unprecedented opportunities for the formation of online business-to-business (B2B) collaborations. In particular, the creation of value-added services by composition of existing ones is gaining a significant momentum. Since many available Web services provide overlapping or identical functionality, albeit with different quality of service (QoS), a choice needs to be made to determine which services are to participate in a given composite service. This paper presents a middleware platform which addresses the issue of selecting Web services for the purpose of their composition in a way that maximizes user satisfaction expressed as utility functions over QoS attributes, while satisfying the constraints set by the user and by the structure of the composite service. Two selection approaches are described and compared: one based on local (task-level) selection of services and the other based on global allocation of tasks to services using integer programming.

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QoS-aware middleware for Web services composition

Since 1985 various evolutionary approaches to multiobjective optimization have been developed, capable of searching for multiple solutions concurrently in a single run. But the few comparative studies of different methods available to date are mostly qualitative and restricted to two approaches. In this paper an extensive, quantitative comparison is presented, applying four multiobjective evolutionary algorithms to an extended 0/1 knapsack problem.

Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study

Today, a variety of heuristic approaches are available to the operations research practitioner. One methodology that has a strong intuitive appeal, a prominent empirical track record, and is trivial to efficiently implement on parallel processors is GRASP (Greedy Randomized Adaptive Search Procedures). GRASP is an iterative randomized sampling technique in which each iteration provides a solution to the problem at hand. The incumbent solution over all GRASP iterations is kept as the final result. There are two phases within each GRASP iteration: the first intelligently constructs an initial solution via an adaptive randomized greedy function; the second applies a local search procedure to the constructed solution in hope of finding an improvement. In this paper, we define the various components comprising a GRASP and demonstrate, step by step, how to develop such heuristics for combinatorial optimization problems. Intuitive justifications for the observed empirical behavior of the methodology are discussed. The paper concludes with a brief literature review of GRASP implementations and mentions two industrial applications.

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Greedy Randomized Adaptive Search Procedures

The clonal selection principle is used to explain the basic features of an adaptive immune response to an antigenic stimulus. It establishes the idea that only those cells that recognize the antigens (Ag's) are selected to proliferate. The selected cells are subject to an affinity maturation process, which improves their affinity to the selective Ag's. This paper proposes a computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response. The general algorithm, named CLONALG, is derived primarily to perform machine learning and pattern recognition tasks, and then it is adapted to solve optimization problems, emphasizing multimodal and combinatorial optimization. Two versions of the algorithm are derived, their computational cost per iteration is presented, and a sensitivity analysis in relation to the user-defined parameters is given. CLONALG is also contrasted with evolutionary algorithms. Several benchmark problems are considered to evaluate the performance of CLONALG and it is also compared to a niching method for multimodal function optimization.

/pdf/learning-and-optimization-using-the-clonal-selection-fjnwsvdguv.pdf

Learning and optimization using the clonal selection principle

We recall some cases of ethical misconduct that occurred in the recent years, and we report a new trick that was recently attempted to cheat the journal in order to obtain the publication of an article through usurped identity.

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A brand new cheating attempt: a case of usurped identity

Combinatorics for modern manufacturing, logistics and supply chains (ECCO XVIII)

https://cris.unibo.it/bitstream/11585/683155/4/Handle-mnlk.pdf

Relaxations and Heuristics for the Multiple Non-Linear Separable Knapsack Problem

https://hal.archives-ouvertes.fr/hal-03008701/document

Lower and upper bounds for the non-linear generalized assignment problem

In 2009 the authors developed an open source Java application and framework, GraphsJ, to help the students in their approach to the study of graph algorithms, by guiding them to understand their logical structures through experiments on numerical instances. After four years, the availability of Java 7 and of new tools suggested the implementation of a new major release. We present a new major release, GraphsJ 3, whose implementation in Java 7 maintains the main characteristics of a useful educational application: Portability, extensibility, ease of use and availability as open source software. The new release provides a redesigned architecture, implemented through cutting-edge languages and technologies and a robust Maven-based build. The presented Java framework constitutes a further step towards the implementation of didactic instruments for the teaching of graph theory. Future developments will include extensions to ease the automatic addition of new algorithms.

Silvano Martello

Papers

A brand new cheating attempt: a case of usurped identity

Combinatorics for modern manufacturing, logistics and supply chains (ECCO XVIII)

Relaxations and Heuristics for the Multiple Non-Linear Separable Knapsack Problem

Lower and upper bounds for the non-linear generalized assignment problem

Graphsj 3: a modern didactic application for graph algorithms