  A population-based optimization algorithm was designed, inspired by two main
thinking modes in philosophy, both based on dialectic concept and
thesis-antithesis paradigm. They impose two different kinds of dialectics.
Idealistic and materialistic antitheses are formulated as optimization models.
Based on the models, the population is coordinated for dialectical
interactions. At the population-based context, the formulated optimization
models are reduced to a simple detection problem for each thinker (particle).
According to the assigned thinking mode to each thinker and her/his
measurements of corresponding dialectic with other candidate particles, they
deterministically decide to interact with a thinker in maximum dialectic with
their theses. The position of a thinker at maximum dialectic is known as an
available antithesis among the existing solutions. The dialectical interactions
at each ideological community are distinguished by meaningful distributions of
step-sizes for each thinking mode. In fact, the thinking modes are regarded as
exploration and exploitation elements of the proposed algorithm. The result is
a delicate balance without any requirement for adjustment of step-size
coefficients. Main parameter of the proposed algorithm is the number of
particles appointed to each thinking modes, or equivalently for each kind of
motions. An additional integer parameter is defined to boost the stability of
the final algorithm in some particular problems. The proposed algorithm is
evaluated by a testbed of 12 single-objective continuous benchmark functions.
Moreover, its performance and speed were highlighted in sparse reconstruction
and antenna selection problems, at the context of compressed sensing and
massive MIMO, respectively. The results indicate fast and efficient performance
in comparison with well-known evolutionary algorithms and dedicated
state-of-the-art algorithms.
