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In a similar way, the binDE uses the oating-point DE individuals to determine a probability for each component These probabilities are then used to generate a bitstring solution from the oating-point vector This bitstring is used by the tness function to determine its quality The resulting tness is then associated with the oating-point representation of the individual Let xi (t) represent a DE individual, with each xij (t) (j = 1, , nx , where nx is the dimension of the binary-valued problem) oating-point number Then, the corresponding bitstring solution, yi (t), is calcualted using yij = where f is the sigmoid function, f (x) = 1 1 + e x (1330) 0 1 if f (xij (t)) 05 if f (xij (t)) < 05 (1329)

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The tness of the individual xi (t) is then simply the tness obtained using the binary representation, yi (t) The binDE algorithm is summarized in Algorithm 1310 Algorithm 1310 Binary Di erential Evolution Algorithm Initialize a population and set control parameter values; t = 0; while stopping condition(s) not true do t = t + 1; Select parent xi (t); Select individuals for reproduction; Produce one o spring, x (t); yi (t) = generated bitstring from xi (t); yi (t) = generated bitstring from xi (t); if f (yi (t)) is better than f (xi (t)) then Replace parent, xi (t), with o spring, xi (t); end else Retain parent, xi (t); end end

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The discussions in the previous sections considered application of DE to unconstrained, single-objective optimization problems, where the tness landscape remains static This section provides a compact overview of adaptations to the DE such that di erent types of optimization problems as summarized in Appendix A can be solved using DE

40 41 42 42 43

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(CP 2)

With reference to Section A6, the following methods have been used to apply DE to solve constrained optimization problems as de ned in De nition A5: Penalty methods (refer to Section A62), where the objective function is adapted by adding a function to penalize solutions that violate constraints [113, 394, 499, 810, 884] Converting the constrained problem to an unconstrained problem by embedding constraints in an augmented Lagrangian (refer to Section A62) [125, 390, 528, 758] Lin et al [529] combines both the penalty and the augmented Lagrangian functions to convert a constrained problem to an unconstrained one In order to preserve the feasibility of initial solutions, Chang and Wu [114] used feasible directions to determine step sizes and search directions By changing the selection operator of DE, infeasible solutions can be rejected, and the repair of infeasible solutions facilitated In order to achieve this, the selection operator accepts an o spring, xi , under the following conditions [34, 56, 498]: if xi satis es all the constraints, and f (xi ) f (xi ), then xi replaces the parent, xi (assuming minimization); if xi is feasible and xi is infeasible, then xi replaces xi ; if both xi and xi are infeasible, then if the number of constraints violated by xi is less than or equal to the number of constraints violated by xi , then xi replaces xi In the case that both the parent and the o spring represent infeasible solutions, there is no selection pressure towards better parts of the tness landscape; rather, towards solutions with the smallest number of violated constraints Boundary constraints are easily enforced by clamping o spring to remain within the given boundaries [34, 164, 498, 499]: if xij (t) < xmin,j or xij > xmax,j otherwise (1331) This restarts the o spring to a random position within the boundaries of the search space xij (t) = xmin,j + U (0, 1)(xmax,j xmin,j ) xij (t).

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