Metaheuristics are the most exciting development in approximate optimization techniques of the last two decades. They have had widespread successes in attacking a variety of difficult combinatorial optimization problems that arise in many practical areas. This bibliography provides a classification of a comprehensive list of 1380 references on the theory and application of metaheuristics. Metaheuristics include but are not limited to constraint logic programming; greedy random adaptive search procedures; natural evolutionary computation; neural networks; non-monotonic search strategies; space-search methods; simulated annealing; tabu search; threshold algorithms and their hybrids. References are presented in alphabetical order under a number of subheadings.

Metaheuristics: A bibliography

State assignment (SA) for finite state machines (FSMs) is one of the main optimization problems in the synthesis of sequential circuits It determines the complexity of its combinational circuit and thus area, delay, testability and power dissipation of its implementation Particle swarm optimization (PSO) is a non-deterministic heuristic that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality PSO optimizes a problem by having a population of candidate solutions called particles, and moving them around in the search-space according to a simple mathematical formulae In this paper, we propose an improved binary particle swarm optimization (BPSO) algorithm and demonstrate its effectiveness in solving the state assignment problem in sequential circuit synthesis targeting area optimization It will be an evident that the proposed BPSO algorithm overcomes the drawbacks of the original BPSO algorithm Experimental results demonstrate the effectiveness of the proposed BPSO algorithm in comparison to other BPSO variants reported in the literature and in comparison to Genetic Algorithm (GA), Simulated Evolution (SimE) and deterministic algorithms like Jedi and Nova

Binary particle swarm optimization (BPSO) based state assignment for area minimization of sequential circuits

The use of assignment to minimise power dissipation and area for finite-state machines is computationally difficult. Most published results show that the reduction of switching activity often trades with area penalty. Two cost functions are introduced and a genetic algorithm is used to optimise the cost functions. Experimental results show a significant reduction of switching activity without area penalty or with smaller area penalty than previous publications.

Genetic algorithm based state assignment for power and area optimisation

In this paper an approach based on an evolutionary algorithm to design synchronous sequential logic circuits with minimum number of logic gates is suggested. The proposed method consists of four main stages. The first stage is concerned with the use of genetic algorithms (GA) for the state assignment problem to compute optimal binary codes for each symbolic state and construct the state transition table of finite state machine (FSM). The second stage defines the subcircuits required to achieve the desired functionality. The third stage evaluates the subcircuits using extrinsic Evolvable Hardware (EHW). During the fourth stage, the final circuit is assembled. The obtained results compare favourably against those produced by manual methods and other methods based on heuristic techniques.

/pdf/evolutionary-algorithms-and-theirs-use-in-the-design-of-53agkpp9me.pdf

Evolutionary Algorithms and Theirs Use in the Design of Sequential Logic Circuits

Partitioning has been shown to be an effective method for synthesis of low power finite state machines. In this approach, an FSM is partitioned into two or more coupled sub-machines such that most of the time only one of the sub-machines is active. In this paper, we present a GA based approach for simultaneous partitioning and state assignment of finite state machines with power reduction as the objective. Experimental results obtained compare favorably with previous works on FSM partitioning, low power state assignment as well as using GA for partitioning alone.

GALLOP: genetic algorithm based low power FSM synthesis by simultaneous partitioning and state assignment

The use of genetic algorithms for the generation of optimal state assignments for synchronous finite state machines (FSM) is proposed. Results are presented to show that, in all examples attempted, the resulting state assignments are better than or at least as good as those produced by SPECTRAL, NOVA and MUSTANG and also closed partition assignments. On average, the genetic algorithm produced assignments with 33% less logic than the best produced by other algorithms.

State assignment of finite state machines using a genetic algorithm

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State assignment of finite state machines using a genetic algorithm