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Particle Swarm Optimisation (PSO) has the advantage of finding, if not the optimum of a continuous problem space, at least a very good position and doing this with modest computational cost. However, as the number of possible optima increases, PSO will only explore a subset of these positions. Techniques such as niching can allow a small number of positions to be explored in parallel but by the time that a problem has become truly deceptive, has many many optima, there is little choice but to explore optima sequentially. PSO, once having converged, has no way of dispersing its particle so as to allow a further convergence, hopefully to a new optima. Random restarts are one way of providing this divergence, this paper suggest another inspired by Extremal Optimisation (EO). The technique proposed allows the particles to disperse by way of positions that are fitter than average. After a while dispersion ceases and PSO takes over again, but since it starts from better than average fitnesses the point it converges to is also better than average. This alternation of algorithms can carry on indefinitely. This paper examines the performance of sequential PSO exploration on a range of problems, some deceptive, some non-deceptive. As predicted, performance on deceptive problems tends to improve significantly with time while performance on non-deceptive problems, which do not have multiple positions with comparable fitness to spread through, does not. [ABSTRACT FROM PUBLISHER]

In order to improve the performance of immune algorithm, chaos optimization is integrated into immune clone selection algorithm. Portion antibodies after decoding are mapped into Lozi's chaos field, and then collect every optical value by each of chaos iteration is added into the new antibody group to improve the antibody-antigen fitness value. This paper analyzes the importance of the selection probability of antibody and gives its varied calculated equation, and adopts the changeable rate of fitness by antibodies 3 times continuous generations to regulate antibody selection probability value adaptively, and gives the concrete steps for the optimization method of hybrid chaos immune algorithm with self-adaptive parameter adjusting. To compare the performance of optimization method of hybrid chaos immune algorithm with self-adaptive parameter adjusting, immune clone selection algorithm and traditional method in the literature, the three methods are used to optimize the main beam cross-section for crane structure. Comparison results indicate that the optimization method of hybrid chaos immune algorithm with self-adaptive parameter adjusting has many advantages such as better self-adaptive capacity, higher computation efficiency and design accuracy. [ABSTRACT FROM PUBLISHER]

This paper presents a design technique of minimizing time for each bit decision and adding appropriate redundancy bits. The technique helps the design of a SAR ADC. We show that the conversion time of proposed SAR ADCs can get faster than that of conventional ones by using the technique. [ABSTRACT FROM PUBLISHER]

We develop a projected-subgradient primal-dual Lagrange optimization for global placement, that can be instantiated with a variety of interconnect models. It decomposes the original non-convex problem into“more convex”sub-problems. It generalizes the recent SimPL, SimPLR and Ripple algorithms and extends them. Empirically, ComPLx outperforms all published placers in runtime and performance on ISPD 2005 and 2006 benchmarks. [ABSTRACT FROM PUBLISHER]