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12) (Mol et al, 2005) The twisted lms show a complex macroscopic deformation owing to the formation of saddlelike geometries, whereas the deformation of the splayed structure is smooth and well controlled (Mol et al, 2005) Wermter and Finkelmann (2001) reported a new type of LC coelastomers composed of LC side chains and LC main-chain polymers (Components 10a c) as network strands Thanks to the direct coupling of the LC main-chain segments to the network anisotropy, the thermoelastic response was increased remarkably with an increase of the concentration of these segments An elongation in the direction of the director by up to a factor of 4, relative to the length of the networks in an isotropic state, was observed in the nematic state (Fig 313) (Wermter and Finkelmann, 2001).

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Ratna and coworkers prepared LCEs with laterally attached side-chain mesogens (11) and studied their thermoelastic properties across a nematic isotropic phase transition (Thomsen et al, 2001) The LCEs showed a large contraction (strain change) of 35% 40% through the phase transition, and the maximum retractive force generated was 270 kPa, which is comparable to that of a skeletal muscle (Fig 314) This increase in the stress during the isostrain measurements is related to the entropy change of the polymer chains and interpreted as a result of the initial work of the wormlike (prolate) to coil transition in the polymer backbones with laterally coupled mesogenic side chains Skeletal muscles are anisotropic, that is, they exhibit contraction and elongation along the ber axis Naciri et al (2003) described a method of preparing LC bers from a side-chain LC terpolymer containing two side-chain mesogens.

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to quantify the performance of an individual in relation to that of other individuals in the population or a competing population Relative tness measures are used in coevolutionary algorithms (refer to 15) It is important to realize at this point that di erent types of optimization problems exist (refer to Section A3), which have an in uence on the formulation of the tness function: Unconstrained optimization problems as de ned in De nition A4, where, assuming that SC = SX , the tness function is simply the objective function Constrained optimization problems as de ned in De nition A5 To solve constrained problems, some EAs change the tness function to contain two objectives: one is the original objective function, and the other is a constraint penalty function (refer to Section A6) Multi-objective optimization problems (MOP) as de ned in De nition A10 MOPs can be solved by using a weighted aggregation approach (refer to Section A8), where the tness function is a weighted sum of all the sub-objectives (refer to equation (A44)), or by using a Pareto-based optimization algorithm Dynamic and noisy problems, where function values of solutions change over time Dynamic tness functions are time-dependent whereas noisy functions usually have an added Gaussian noise component Dynamic problems are e ned in De nition A16 Equation (A58) gives a noisy function with an additive Gaussian noise component As a nal comment on the tness function, it is important to emphasize its role in an EA The evolutionary operators, eg selection, crossover, mutation and elitism, usually make use of the tness evaluation of chromosomes For example, selection operators are inclined towards the most- t individuals when selecting parents for crossover, while mutation leans towards the least- t individuals.

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Early results of the application of the PSO to type I environments (refer to Section A9) with small spatial severity showed that the PSO has an implicit ability to track changing optima [107, 228, 385] Each particle progressively converges on a point on the line that connects its personal best position with the global best position [863, 870] The trajectory of a particle can be described by a sinusoidal wave with diminishing amplitude around the global best position [651, 652] If there is a small change in the ocation of an optimum, it is likely that one of these oscillating particles will discover the new, nearby optimum, and will pull the other particles to swarm around the new optimum However, if the spatial severity is large, causing the optimum to be displaced outside the radius of the contracting swarm, the PSO will fail to locate the new optimum due to loss of diversity In such cases mechanisms need to be employed to increase the swarm diversity Consider spatial changes where the value of the optimum remains the same after the change, ie f (x (t)) = f (x (t + 1)), with x (t) = x (t + 1) Since the tness remains the same, the global best position does not change, and remains at the old optimum Similarly, if f (x (t)) > f (x (t+1)), assuming minimization, the global best position will also not change Consequently, the PSO will fail to track such a changing minimum This problem can be solved by re-evaluating the tness of particles at time t + 1 and updating global best and personal best positions However, keep in mind that the same problem as discussed above may still occur if the optimum is displaced outside the radius of the swarm One of the goals of optimization algorithms for dynamic environments is to locate the optimum and then to track it The self-adaptation ability of the PSO to track optima (as discussed above) assumes that the PSO did not converge to an equilibrium state in its rst goal to locate the optimum When the swarm reaches an equilibrium (ie converged to a solution), vi = 0 The particles have no momentum and the contributions from the cognitive and social components are zero The particles will remain in this stable state even if the optimum does change In the case of dynamic environments, it is possible that the swarm reaches an equilibrium if the temporal severity is low (in other words, the time between consecutive changes is large) It is therefore important to read the literature on PSO for dynamic environments with this aspect always kept in mind The next aspects to consider are the in uence of particle memory, velocity clamping and the inertia weight The question to answer is: to what extent do these parameters and characteristics of the PSO limit or promote tracking of changing optima These aspects are addressed next: Each particle has a memory of its best position found thus far, and velocity is adjusted to also move towards this position Similarly, each particle retains information about the global best (or local best) position When the environment changes this information becomes stale If, after an environment change, particles are still allowed to make use of this, now stale, information, they are.

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