Paper, 4 p, IAENG International Journal of Applied Mathematics. The paper attempts to solve the generalized Assignment problem through genetic algorithm and simulated annealing. The generalized assignment problem is basically the N men- N jobs problem where a single job can be assigned to only one person in such a way that the overall cost of assignment is minimized. While solving this problem through genetic algorithm (GA), a unique encoding scheme is used together with Partially Matched Crossover (PMX). The population size can also be varied in each iteration. In simulated annealing (SA) method, an exponential cooling schedule based on Newtonian cooling process is employed and experimentation is done on choosing the number of iterations (m) at each step. The source codes for the above have been developed in C language and compiled in GCC. Several test cases have been taken and the results obtained from both the methods have been tabulated and compared against the results obtained by coding in AMPL. Metaheuristics, in their original definition, are solution methods that orchestrate an interaction between local improvement procedures and higher level strategies to create a process capable of escaping from local optima and performing a robust search of a solution space. Over time, these methods have also come to include any procedures that employ strategies for overcoming the trap of local optimality in complex solution spaces, especially those procedures that utilize one or more neighborhood structures as a means of defining admissible moves to transition from one solution to another, or to build or destroy solutions in constructive and destructive processes. The degree to which neighborhoods are exploited varies according to the type of procedure. In the case of certain population-based procedures, such as genetic al- rithms, neighborhoods are implicitly (and somewhat restrictively) defined by reference to replacing components of one solution with those of another, by variously chosen rules of exchange popularly given the name of “crossover. ” In other population-based methods, based on the notion of path relinking, neighborhood structures are used in their full generality, including constructive and destructive neighborhoods as well as those for transitioning between (complete) solutions. Certain hybrids of classical evoluti- ary approaches, which link them with local search, also use neighborhood structures more fully, though apart from the combination process itself. This handbook provides both the research and practitioner communities with a comprehensive coverage of the metaheuristic methodologies that have proven to be successful in a wide variety of real-world problem settings. Moreover, it is these metaheuristic strategies that hold particular promise for success in the future. The various chapters serve as stand alone presentations giving both the necessary background underpinnings as well as practical guides for implementation. In most settings a problem solver has an option as to which metaheuristic approach should be adopted for the problem at hand. Alternative methodologies typically exist that could be employed to produce high quality solutions. Often it becomes a matter of choosing one of several approaches that could be adopted. The very nature of metaheuristics invites an analyst to modify basic methods in response to problem characteristics, past experiences, and personal preferences. The chapters in this handbook are designed to facilitate this as well.
