2,210 research outputs found
When Hillclimbers Beat Genetic Algorithms in Multimodal Optimization
It has been shown in the past that a multistart hillclimbing strategy
compares favourably to a standard genetic algorithm with respect to solving
instances of the multimodal problem generator. We extend that work and verify
if the utilization of diversity preservation techniques in the genetic
algorithm changes the outcome of the comparison. We do so under two scenarios:
(1) when the goal is to find the global optimum, (2) when the goal is to find
all optima.
A mathematical analysis is performed for the multistart hillclimbing
algorithm and a through empirical study is conducted for solving instances of
the multimodal problem generator with increasing number of optima, both with
the hillclimbing strategy as well as with genetic algorithms with niching.
Although niching improves the performance of the genetic algorithm, it is still
inferior to the multistart hillclimbing strategy on this class of problems.
An idealized niching strategy is also presented and it is argued that its
performance should be close to a lower bound of what any evolutionary algorithm
can do on this class of problems
When hillclimbers beat genetic algorithms in multimodal optimization
This paper investigates the performance of multistart next ascent hillclimbing and well-known evolutionary algorithms incorporating diversity preservation techniques on instances of the multimodal problem generator. This generator induces a class of problems in the bitstringdomain which is interesting to study from a theoretical perspective in the context of multimodal optimization, as it is a generalization of the classical OneMax and TwoMax functions for an arbitrary number of peaks. An average-case runtime analysis for multistart next ascent hill-climbing is presented for uniformly distributed equal-height instances of this class of problems. It is shown empirically that conventional niching and mating restriction techniques incorporated in an evolutionary algorithm are not sufficient to make them competitive with the hillclimbing strategy. We conjecture the reason for this behaviour is the lack of structure in the space of local optima on instances of this problem class, which makes an optimization algorithm unable to exploit information from one optimum to infer where another optimum might be. When no such structure exist, it seems that the best strategy for discovering all
optima is a brute-force one. Overall, our study gives insights with respect to the adequacy of hillclimbers and evolutionary algorithms for multimodal optimization, depending on properties of the fitness landscape.info:eu-repo/semantics/publishedVersio
GNBG: A Generalized and Configurable Benchmark Generator for Continuous Numerical Optimization
As optimization challenges continue to evolve, so too must our tools and
understanding. To effectively assess, validate, and compare optimization
algorithms, it is crucial to use a benchmark test suite that encompasses a
diverse range of problem instances with various characteristics. Traditional
benchmark suites often consist of numerous fixed test functions, making it
challenging to align these with specific research objectives, such as the
systematic evaluation of algorithms under controllable conditions. This paper
introduces the Generalized Numerical Benchmark Generator (GNBG) for
single-objective, box-constrained, continuous numerical optimization. Unlike
existing approaches that rely on multiple baseline functions and
transformations, GNBG utilizes a single, parametric, and configurable baseline
function. This design allows for control over various problem characteristics.
Researchers using GNBG can generate instances that cover a broad array of
morphological features, from unimodal to highly multimodal functions, various
local optima patterns, and symmetric to highly asymmetric structures. The
generated problems can also vary in separability, variable interaction
structures, dimensionality, conditioning, and basin shapes. These customizable
features enable the systematic evaluation and comparison of optimization
algorithms, allowing researchers to probe their strengths and weaknesses under
diverse and controllable conditions
Econometric and Environmental Optimization of Combined Cooling, Heating and Power Plant Operation
Combined Cooling, Heat and Power (CCHP) systems have great potential to recover low-grade thermal energy, resulting in higher energy efficiency, reduced emission rates, lower operating costs and a higher level of energy security. In order to fully realize the benefits of CCHP systems in terms of reduced cost and carbon dioxide emissions, effective optimization and control strategies are required. This work presents an approach for optimizing the operation of the CCHP system using a detailed network energy flow model solved by genetic algorithm. The optimal energy dispatch algorithm provides operational signals associated with resource allocation ensuring that the systems meet campus electricity, heating, and cooling demands. The performance of the system will be compared and evaluated with respect to economic and environmental benefits
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