17,491 research outputs found
PasMoQAP: A Parallel Asynchronous Memetic Algorithm for solving the Multi-Objective Quadratic Assignment Problem
Multi-Objective Optimization Problems (MOPs) have attracted growing attention
during the last decades. Multi-Objective Evolutionary Algorithms (MOEAs) have
been extensively used to address MOPs because are able to approximate a set of
non-dominated high-quality solutions. The Multi-Objective Quadratic Assignment
Problem (mQAP) is a MOP. The mQAP is a generalization of the classical QAP
which has been extensively studied, and used in several real-life applications.
The mQAP is defined as having as input several flows between the facilities
which generate multiple cost functions that must be optimized simultaneously.
In this study, we propose PasMoQAP, a parallel asynchronous memetic algorithm
to solve the Multi-Objective Quadratic Assignment Problem. PasMoQAP is based on
an island model that structures the population by creating sub-populations. The
memetic algorithm on each island individually evolve a reduced population of
solutions, and they asynchronously cooperate by sending selected solutions to
the neighboring islands. The experimental results show that our approach
significatively outperforms all the island-based variants of the
multi-objective evolutionary algorithm NSGA-II. We show that PasMoQAP is a
suitable alternative to solve the Multi-Objective Quadratic Assignment Problem.Comment: 8 pages, 3 figures, 2 tables. Accepted at Conference on Evolutionary
Computation 2017 (CEC 2017
Credit Assignment in Adaptive Evolutionary Algorithms
In this paper, a new method for assigning credit to search\ud
operators is presented. Starting with the principle of optimizing\ud
search bias, search operators are selected based on an ability to\ud
create solutions that are historically linked to future generations.\ud
Using a novel framework for defining performance\ud
measurements, distributing credit for performance, and the\ud
statistical interpretation of this credit, a new adaptive method is\ud
developed and shown to outperform a variety of adaptive and\ud
non-adaptive competitors
Evolutionary Synthesis of HVAC System Configurations: Algorithm Development.
This paper describes the development of an optimization procedure for the synthesis of novel heating, ventilating, and air-conditioning (HVAC) system configurations. Novel HVAC system designs can be synthesized using model-based optimization methods. The optimization problem can be considered as having three sub-optimization problems; the choice of a component set; the design of the topological connections between the components; and the design of a system operating strategy. In an attempt to limit the computational effort required to obtain a design solution, the approach adopted in this research is to solve all three sub-problems simultaneously. Further, the computational effort has been limited by implementing simplified component models and including the system performance evaluation as part of the optimization problem (there being no need in this respect to simulation the system performance). The optimization problem has been solved using a Genetic Algorithm (GA), with data structures and search operators that are specifically developed for the solution of HVAC system optimization problems (in some instances, certain of the novel operators may also be used in other topological optimization problems. The performance of the algorithm, and various search operators has been examined for a two-zone optimization problem (the objective of the optimization being to find a system design that minimizes the system energy use). In particular, the performance of the algorithm in finding feasible system designs has been examined. It was concluded that the search was unreliable when the component set was optimized, but if the component set was fixed as a boundary condition on the search, then the algorithm had an 81% probability of finding a feasible system design. The optimality of the solutions is not examined in this paper, but is described in an associated publication. It was concluded that, given a candidate set of system components, the algorithm described here provides an effective tool for exploring the novel design of HVAC systems. (c) HVAC & R journa
Methods for many-objective optimization: an analysis
Decomposition-based methods are often cited as the
solution to problems related with many-objective optimization. Decomposition-based methods employ a scalarizing function to reduce a many-objective problem into a set of single objective problems, which upon solution yields a good approximation of the set of optimal solutions. This set is commonly referred to as
Pareto front. In this work we explore the implications of using decomposition-based methods over Pareto-based methods from a probabilistic point of view. Namely, we investigate whether there is an advantage of using a decomposition-based method, for example using the Chebyshev scalarizing function, over Paretobased methods
A Coevolutionary Particle Swarm Algorithm for Bi-Level Variational Inequalities: Applications to Competition in Highway Transportation Networks
A climate of increasing deregulation in traditional highway transportation,
where the private sector has an expanded role in the provision of traditional
transportation services, provides a background for practical policy issues to be investigated.
One of the key issues of interest, and the focus of this chapter, would
be the equilibrium decision variables offered by participants in this market. By assuming
that the private sector participants play a Nash game, the above problem can
be described as a Bi-Level Variational Inequality (BLVI). Our problem differs from
the classical Cournot-Nash game because each and every player’s actions is constrained
by another variational inequality describing the equilibrium route choice of
users on the network. In this chapter, we discuss this BLVI and suggest a heuristic
coevolutionary particle swarm algorithm for its resolution. Our proposed algorithm
is subsequently tested on example problems drawn from the literature. The numerical
experiments suggest that the proposed algorithm is a viable solution method for
this problem
Balancing Selection Pressures, Multiple Objectives, and Neural Modularity to Coevolve Cooperative Agent Behavior
Previous research using evolutionary computation in Multi-Agent Systems
indicates that assigning fitness based on team vs.\ individual behavior has a
strong impact on the ability of evolved teams of artificial agents to exhibit
teamwork in challenging tasks. However, such research only made use of
single-objective evolution. In contrast, when a multiobjective evolutionary
algorithm is used, populations can be subject to individual-level objectives,
team-level objectives, or combinations of the two. This paper explores the
performance of cooperatively coevolved teams of agents controlled by artificial
neural networks subject to these types of objectives. Specifically, predator
agents are evolved to capture scripted prey agents in a torus-shaped grid
world. Because of the tension between individual and team behaviors, multiple
modes of behavior can be useful, and thus the effect of modular neural networks
is also explored. Results demonstrate that fitness rewarding individual
behavior is superior to fitness rewarding team behavior, despite being applied
to a cooperative task. However, the use of networks with multiple modules
allows predators to discover intelligent behavior, regardless of which type of
objectives are used
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