Download Applications of Evolutionary Computation: 17th European by Anna I. Esparcia-Alcázar, Antonio M. Mora PDF

By Anna I. Esparcia-Alcázar, Antonio M. Mora

This ebook constitutes the completely refereed post-conference complaints of the overseas convention at the purposes of Evolutionary Computation, EvoApplications 2014, held in Granada, Spain, in April 2014, colocated with the Evo* 2014 occasions EuroGP, EvoCOP, and EvoMUSART.

The seventy nine revised complete papers awarded have been rigorously reviewed and chosen from 128 submissions. EvoApplications 2014 consisted of the subsequent thirteen tracks: EvoCOMNET (nature-inspired ideas for telecommunication networks and different parallel and allotted systems), EvoCOMPLEX (evolutionary algorithms and intricate systems), EvoENERGY (evolutionary computation in power applications), EvoFIN (evolutionary and normal computation in finance and economics), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in picture research, sign processing, and trend recognition), EvoINDUSTRY (nature-inspired strategies in business settings), EvoNUM (bio-inspired algorithms for non-stop parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for danger administration, protection and defence applications), EvoROBOT (evolutionary computation in robotics), EvoSTOC (evolutionary algorithms in stochastic and dynamic environments), and EvoBio (EC and similar suggestions in bioinformatics and computational biology).

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Given the previous characterization of the multiband uncertainty set, the maximum positive deviation of traffic DEVet (x, D) of a constraint (3) can be found by solving a binary linear program (see [4] for details). Since the polytope associated with the binary program is shown to be integral, by considering its relaxation and by exploiting strong duality, it is possible to reformulate the original trivial robust counterpart as the following linear and compact robust counterpart (we refer the reader to [4] for a formal proof of the result): min (Rob-MP-CNDP) γet yet e∈E t∈T d¯ct xcpt + c∈C p∈Pc : e∈p t + θetk wetk + k∈K zecpt ≤ φ c∈C p∈Pc : e∈p wetk + zecpt ≥ δctk xcpt yeτ e ∈ E, t ∈ T τ =1 e ∈ E, c ∈ C, p ∈ Pc : e ∈ p, t ∈ T, k ∈ K wetk ∈ R e ∈ E, t ∈ T, k ∈ K zecpt ≥ 0 e ∈ E, c ∈ C, p ∈ Pc : e ∈ p, t ∈ T (4) (5) (6) A Hybrid Primal Heuristic for Robust Multiperiod Network Design xcpt = 1 21 c ∈ C, t ∈ T p∈Pc xcpt ∈ {0, 1} c ∈ C, p ∈ Pc , t ∈ T yet ∈ Z+ e ∈ E, t ∈ T , This formulation includes additional constraints (4) and variables (5),(6) which are derived from the dualization operation that allow to linearly reformulate the original (non-linear) problem including the term DEVet (x, D) in each capacity constraint (see [4] for details).

UNTIL time limit is reached DO (a) FOR μ := 1 TO m DO i. build a complete routing state; ii. derive a complete feasible solution for (Rob-MP-CNDP); END FOR (b) Update τij (t) according to (7). 3. apply mod-RINS to the best feasible solution. 5 Experimental Results We tested the performance of our hybrid algorithm on a set of 15 instances based on realistic network topologies from the SNDlib [10] defined in collaboration with industrial partners from former and ongoing projects. 4. All the instances led to very large and hard to solve robust multiperiod network design problems.

Example 2 (infeasibility caused by deviations). e. we have installed 100+150 Mb of capacity). This capacity dimensioning neglects that the demands may deviate up to 10%. It is sufficient that one demand increases, while the other remains the same to violate the capacity constraint of the link, making the design solution infeasible in practice. Over the years, many methods such as Stochastic Programming and Robust Optimization have been proposed in literature for dealing with data uncertainty in optimization problems.

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