By Yacine Laalaoui, Nizar Bouguila
This booklet offers a variety of fresh functions of synthetic Intelligence in details and communique applied sciences resembling seek and Optimization tools, computer studying, info illustration and Ontologies, and Multi-agent structures. the most target of this ebook is to assist info and verbal exchange applied sciences (ICT) practitioners in handling successfully their systems utilizing AI instruments and strategies and to supply them with adequate synthetic Intelligence historical past to house real-life difficulties.
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Life. 5–3, 137–172 (1999) 2. : Information Retrieval. Information Retrieval Group University of Glasgow, Glasgow (1979) 3. : A new rank based version of the ant system, a computational study. Technical Report POM -03/97, Institute of Management Science, University of Vienna (1997) 4. : A new ACO model integrating evolutionary computation concepts: the best-worst ant system. In: From Ant Colonies to Artificial Ants, ANTS 2000, pp. 22–29 (2000) 50 Y. Drias and S. Kechid 5. : Ant algorithms for the traveling salesman problem.
38 Y. Drias and S. Kechid Fig. 4 Building and Improving a Solution Each ant performs the task of exploring the best document in a sub-collection. The method designed for this aim is described through the procedure called build_AS. It merely chooses a solution from its neighbourhood with probability p defined in formula (1). 1; pheromone initialization 3: end for 4: select at random a solution s from N ; a document namely s 5: best := bestsol := s; 6: for i=1 to MaxIter do 7: for k=1 to NbAnts do 8: generate a random initial solution s; 9: sol[k] = s; 10: s’ := build_AS (s) ; 11: sol[k] := s’; update the best solution of k 12: update the online pheromone pher o[k] using formulas (2) and (3); 13: if f (s ) > f (best) then then best := s’; f is the similarity function 14: end if 15: end for 16: if f (best) > f (bestsol) then bestsol := best; 17: end if 18: apply online-update of pheromone; 19: end for 20: return (bestsol); 21: end procedure After its construction, each solution undergoes an improvement of its quality by applying the procedure tabu-search.
Klusáˇcek and H. Rudová Fig. 3 The avg. wait time and the bounded slowdown (left), the avg. response time (middle) and the mean and the standard deviation of NUWTo values (right) the queue with different jobs of other users, backfilling may then often work more efficiently. The more aggressive EASY-F again outperforms CONS-F by producing better slowdowns, wait and response times for all workloads. Concerning fairness, EASY and CONS are usually outperformed by their fair-share based variants (EASYF, CONS-F), which is natural since EASY and CONS do not apply any technique A Metaheuristic for Optimizing the Performance and the Fairness … 23 to fairly balance resource usage among users.