14 research outputs found
Effective Solution of University Course Timetabling using Particle Swarm Optimizer based Hyper Heuristic approach
عادة ما تكون مشكلة الجدول الزمني للمحاضرات الجامعية (UCTP) هي مشكلة تحسين الإندماجية. يستغرق الأمر جهود يدوية لعدة أيام للوصول إلى جدول زمني مفيد ، ولا تزال النتائج غير جيدة بما يكفي. تُستخدم طرق مختلفة من (الإرشاد أو الإرشاد المساعد) لحل UCTP بشكل مناسب. لكن هذه الأساليب عادةً ما تعطي حلول محدودة. يعالج إطار العمل الاسترشادي العالي هذه المشكلة المعقدة بشكل مناسب. يقترح هذا البحث استخدام محسن سرب الجسيمات استنادا على منهجية الإرشاد العالي (HH PSO) لمعالجة مشكلة الجدول الزمني للمحاضرات الجامعية (UCTP) . محسن سرب الجسيمات PSO يستخدام كطريقة ذات مستوى عالي لتحديد تسلسل الاستدلال ذي المستوى المنخفض (LLH) والذي من ناحية أخرى يستطيع توليد الحل الأمثل. لنهج المقترح يقسم الحل إلى مرحلتين (المرحلة الأولية ومرحلة التحسين). قمنا بتطوير LLH جديد يسمى "أقل عدد ممكن من الغرف المتبقية" لجدولة الأحداث. يتم استخدام مجموعتي بيانات مسابقة الجدول الزمني الدولية (ITC) ITC 2002 و ITC 2007 لتقييم الطريقة المقترحة. تشير النتائج الأولية إلى أن الإرشاد منخفض المستوى المقترح يساعد في جدولة الأحداث في المرحلة الأولية. بالمقارنة مع LLH الأخرى ، الطريقة LLH المقترحة جدولت المزيد من الأحداث لـ 14 و 15 من حالات البيانات من 24 و 20 حالة بيانات من ITC 2002 و ITC 2007 ، على التوالي. تظهر الدراسة التجريبية أن HH PSO تحصل على معدل خرق أقل للقيود في سبع وستة حالات بيانات من ITC 2007 و ITC 2002 ، على التوالي. واستنتج هذا البحث أن LLH المقترحة يمكن أن تحصل على حل معقول وملائم إذا تم تحديد الأولوياتThe university course timetable problem (UCTP) is typically a combinatorial optimization problem. Manually achieving a useful timetable requires many days of effort, and the results are still unsatisfactory. unsatisfactory. Various states of art methods (heuristic, meta-heuristic) are used to satisfactorily solve UCTP. However, these approaches typically represent the instance-specific solutions. The hyper-heuristic framework adequately addresses this complex problem. This research proposed Particle Swarm Optimizer-based Hyper Heuristic (HH PSO) to solve UCTP efficiently. PSO is used as a higher-level method that selects low-level heuristics (LLH) sequence which further generates an optimal solution. The proposed approach generates solutions into two phases (initial and improvement). A new LLH named “least possible rooms left” has been developed and proposed to schedule events. Both datasets of international timetabling competition (ITC) i.e., ITC 2002 and ITC 2007 are used to evaluate the proposed method. Experimental results indicate that the proposed low-level heuristic helps to schedule events at the initial stage. When compared with other LLH’s, the proposed LLH schedule more events for 14 and 15 data instances out of 24 and 20 data instances of ITC 2002 and ITC 2007, respectively. The experimental study shows that HH PSO gets a lower soft constraint violation rate on seven and six data instances of ITC 2007 and ITC 2002, respectively. This research has concluded the proposed LLH can get a feasible solution if prioritized
An Extended Jump Functions Benchmark for the Analysis of Randomized Search Heuristics
Jump functions are the {most-studied} non-unimodal benchmark in the theory of
randomized search heuristics, in particular, evolutionary algorithms (EAs).
They have significantly improved our understanding of how EAs escape from local
optima. However, their particular structure -- to leave the local optimum one
can only jump directly to the global optimum -- raises the question of how
representative such results are.
For this reason, we propose an extended class \textsc{Jump}_{k,\delta} of
jump functions that contain a valley of low fitness of width starting
at distance from the global optimum. We prove that several previous results
extend to this more general class: for all {} and
, the optimal mutation rate for the ~EA is
, and the fast ~EA runs faster than the classical
~EA by a factor super-exponential in . However, we also observe
that some known results do not generalize: the randomized local search
algorithm with stagnation detection, which is faster than the fast ~EA
by a factor polynomial in on \textsc{Jump}_k, is slower by a factor
polynomial in on some \textsc{Jump}_{k,\delta} instances.
Computationally, the new class allows experiments with wider fitness valleys,
especially when they lie further away from the global optimum.Comment: Extended version of a paper that appeared in the proceedings of GECCO
2021. To appear in Algorithmic
Self-adjusting Population Sizes for Non-elitist Evolutionary Algorithms:Why Success Rates Matter
Evolutionary algorithms (EAs) are general-purpose optimisers that come with several
parameters like the sizes of parent and offspring populations or the mutation rate. It is
well known that the performance of EAs may depend drastically on these parameters.
Recent theoretical studies have shown that self-adjusting parameter control mechanisms that tune parameters during the algorithm run can provably outperform the best
static parameters in EAs on discrete problems. However, the majority of these studies
concerned elitist EAs and we do not have a clear answer on whether the same mechanisms can be applied for non-elitist EAs. We study one of the best-known parameter
control mechanisms, the one-fifth success rule, to control the offspring population
size λ in the non-elitist (1, λ) EA. It is known that the (1, λ) EA has a sharp threshold
with respect to the choice of λ where the expected runtime on the benchmark function OneMax changes from polynomial to exponential time. Hence, it is not clear
whether parameter control mechanisms are able to find and maintain suitable values
of λ. For OneMax we show that the answer crucially depends on the success rate s
(i. e. a one-(s + 1)-th success rule). We prove that, if the success rate is appropriately
small, the self-adjusting (1, λ) EA optimises OneMax in O(n) expected generations
and O(n log n) expected evaluations, the best possible runtime for any unary unbiased
black-box algorithm. A small success rate is crucial: we also show that if the success
rate is too large, the algorithm has an exponential runtime on OneMax and other
functions with similar characteristics
Lazy Parameter Tuning and Control:Choosing All Parameters Randomly from a Power-Law Distribution
Most evolutionary algorithms have multiple parameters and their values
drastically affect the performance. Due to the often complicated interplay of
the parameters, setting these values right for a particular problem (parameter
tuning) is a challenging task. This task becomes even more complicated when the
optimal parameter values change significantly during the run of the algorithm
since then a dynamic parameter choice (parameter control) is necessary.
In this work, we propose a lazy but effective solution, namely choosing all
parameter values (where this makes sense) in each iteration randomly from a
suitably scaled power-law distribution. To demonstrate the effectiveness of
this approach, we perform runtime analyses of the
genetic algorithm with all three parameters chosen in this manner. We show that
this algorithm on the one hand can imitate simple hill-climbers like the
EA, giving the same asymptotic runtime on problems like OneMax,
LeadingOnes, or Minimum Spanning Tree. On the other hand, this algorithm is
also very efficient on jump functions, where the best static parameters are
very different from those necessary to optimize simple problems. We prove a
performance guarantee that is comparable, sometimes even better, than the best
performance known for static parameters. We complement our theoretical results
with a rigorous empirical study confirming what the asymptotic runtime results
suggest.Comment: Extended version of the paper accepted to GECCO 2021, including all
the proofs omitted in the conference versio
When hypermutations and ageing enable artificial immune systems to outperform evolutionary algorithms
We present a time complexity analysis of the Opt-IA artificial immune system (AIS). We first highlight the power and limitations of its distinguishing operators (i.e., hypermutations with mutation potential and ageing) by analysing them in isolation. Recent work has shown that ageing combined with local mutations can help escape local optima on a dynamic optimisation benchmark function. We generalise this result by rigorously proving that, compared to evolutionary algorithms (EAs), ageing leads to impressive speed-ups on the standard Image 1 benchmark function both when using local and global mutations. Unless the stop at first constructive mutation (FCM) mechanism is applied, we show that hypermutations require exponential expected runtime to optimise any function with a polynomial number of optima. If instead FCM is used, the expected runtime is at most a linear factor larger than the upper bound achieved for any random local search algorithm using the artificial fitness levels method. Nevertheless, we prove that algorithms using hypermutations can be considerably faster than EAs at escaping local optima. An analysis of the complete Opt-IA reveals that it is efficient on the previously considered functions and highlights problems where the use of the full algorithm is crucial. We complete the picture by presenting a class of functions for which Opt-IA fails with overwhelming probability while standard EAs are efficient